diff --git a/067-FabricLakehouse/.wordlist.txt b/067-FabricLakehouse/.wordlist.txt new file mode 100644 index 0000000000..cfab0fdc48 --- /dev/null +++ b/067-FabricLakehouse/.wordlist.txt @@ -0,0 +1,33 @@ +Yarr +yarr +PySpark +TMTOWTDI +DAX +dataflow +ye'll +BOM +Nemo +bom +spatialdata +geojson +ECMWF +planetarycomputer +stac +ipynb +WAM +dimensionally +datawarehousing +BAFTA +Leeuwin +Batavia +Boorloo +Liesel +Whadjuk +FabricTrial +getpowerbi +workspace's +buddied +shapefiles +hoc +impactful +shtml diff --git a/067-FabricLakehouse/Coach/Lectures.pptx b/067-FabricLakehouse/Coach/Lectures.pptx new file mode 100644 index 0000000000..b0656bcf35 Binary files /dev/null and b/067-FabricLakehouse/Coach/Lectures.pptx differ diff --git a/067-FabricLakehouse/Coach/README.md b/067-FabricLakehouse/Coach/README.md new file mode 100644 index 0000000000..5fa7a297dd --- /dev/null +++ b/067-FabricLakehouse/Coach/README.md @@ -0,0 +1,269 @@ +# What The Hack - Fabric Lakehouse - Coach Guide + +## Introduction + +Welcome to the coach's guide for the Fabric Lakehouse What The Hack. Here you will find links to specific guidance for coaches for each of the challenges. + +This hack includes an awesome [lecture presentation](Lectures.pptx) that features short presentations to introduce key topics associated with each challenge. It is recommended that the host present each short presentation before attendees kick off that challenge. + +You may also want to customise the deck to include logistics, timings, you and other coaches details, and other information specific to your event. Additional sea-themed puns are also encouraged. Yarr... + +**NOTE:** If you are a Hackathon participant, this is the answer guide. Don't cheat yourself by looking at these during the hack! Go learn something. :) + +## Coach's Guides + +- Challenge 00: **[Prerequisites - Grab your fins and a full tank!](Solution-00.md)** + - Provision your Fabric Lakehouse +- Challenge 01: **[Finding Data](Solution-01.md)** + - Head out into open waters to find your data +- Challenge 02: **[Land ho!](Solution-02.md)** + - Land your data in your Fabric Lakehouse +- Challenge 03: **[Swab the Decks!](Solution-03.md)** + - Clean and combine your datasets ready for analysis +- Challenge 04: **[Make a Splash](Solution-04.md)** + - Build a data story to bring your findings to life +- Challenge 05: **[Giant Stride](Solution-05.md)** + - Take a giant stride and share your data story with the world + +## Coach Prerequisites + +This hack has pre-reqs that a coach is responsible for understanding and/or setting up BEFORE hosting an event. Please review the [What The Hack Hosting Guide](https://aka.ms/wthhost) for information on how to host a hack event. + +The guide covers the common preparation steps a coach needs to do before any What The Hack event. + +### About the Audience + +Students will most likely have a wide range of backgrounds and experience ranging from Data Engineers comfortable with PySpark, T-SQL and data lake technology, to business analysts with little or no coding experience. The hack is intended to be accessible to all, but coaches should be aware of the following: + +- The hack is designed to be completed by teams of 2-4 students working together. Coaches should consider how to arrange students into teams to ensure a mix of skills and experience. However, for an event with more experienced (or daring) students the hack can be completed individually. +- The hack is designed to be completed in a linear fashion. Encourage students to work outside of their areas of expertise, but be aware that some challenges may be more difficult for some students than others. Use your discretion - students may commence challenges in parallel. For example, a Power BI reporting expert may wish to start Challenge 4 while the other team members are data wrangling in Challenge 2/3. + +### Duration & Agenda + +Ideally this hack can be completed in a day, but it is highly dependant on student profiles. For a more relaxed pace for a less experienced group, consider running the hack over 2 days - one day for challenges 1-3 and the second day for challenges 3-5. + +Times are very fluid and almost certainly will vary, but a *very* indicative agenda for a 1 day hack is as follows: + +|Time|Duration|Activity| +|----|--------|--------| +|9:30 AM|15 mins|Welcome!| +|9:45 AM|30 mins|An Overview of Microsoft Fabric| +|10:15 AM|15 mins|About The Hack & Challenge 0 - Get Your Gear Ready| +|10:30 AM|15 mins|Break| +|10:45 AM|45 mins|Challenge 1 - Finding Data| +|11:30 AM|60 mins|Challenge 2 - Land Ho!| +|12:00 PM|60 mins|Lunch| +|1:00 PM|30 min|Challenge 2 cont.| +|1:30 PM|60 mins|Challenge 3 - Swab the Decks!| +|2:30 PM|15 mins|Break| +|2:45 PM|60 mins|Challenge 4 - Make A Splash!| +|3:45 PM|30 mins|Challenge 5 - Giant Stride| +|4:15 PM|15 mins|Wrap Up| + +### Additional Coach Prerequisites + +This hack as been left very open ended - [TMTOWTDI](https://perl.fandom.com/wiki/TIMTOWTDI). Coaches should be familiar with the following technologies and concepts: + +- Microsoft Fabric (duh!) +- Python / PySpark +- Dataflow Gen 2 / M / Power Query +- Power BI / Data Analysis Expressions (DAX) + +Coaches should deploy the solution files before the event to ensure they are familiar with the approach and the technologies used. Coaches should also be familiar with the datasets and source government agency websites used in the example solutions. Links are provided in the various [Solutions](./Solutions). + +### Coach Resources + +- [What is Microsoft Fabric?](https://aka.ms/learnfabric) +- [Microsoft Fabric Blog](https://aka.ms/FabricBlog) +- [Import Existing Notebooks](https://learn.microsoft.com/en-us/fabric/data-engineering/how-to-use-notebook#import-existing-notebooks) +- [Importing a dataflow gen2 template](https://learn.microsoft.com/en-us/fabric/data-factory/move-dataflow-gen1-to-dataflow-gen2) + +- [Fabric (trial) Known Issues](https://learn.microsoft.com/en-gb/fabric/get-started/fabric-known-issues) + +### Student Resources + +Always refer students to the [What The Hack website](https://aka.ms/wth) for the student guide: [https://aka.ms/wth](https://aka.ms/wth) + +Coaches are should generate a complete dataset ahead of time and use this to become familiar with each, giving consideration on how they can be used to with the cohort. Coaches are strongly recommended to **regenerate on the day of the event** to refresh weather forecast data. + +Depending on the composition of the student group, coaches may wish to provide all **(or none)** of the datasets to students. For example, a group of data engineers may be comfortable working with Python to build an complete end to end pipeline, including automating ingesting from source, but a group of business analysts may require data in a slightly cleaner format to enable them complete the challenges. Use your discretion to provide the most appropriate support, this is supposed to be a fun learning experience after all! + +### A Note on the Provided Data + +Students may ask about data provided by the coach (``resources.zip``). First, it is important to note that the data provided is not intended to be used as a solution to the hack. Discourage students from 'peeking' at the data with your best pirate captain voice. + +*"Avast ye sea dogs, don't be peeking at me data files or ye'll be walking the plank! But if ye have fallen overboard and be lost adrift, then these be here to saves you. Yarr..."* + +> Yes, I know I'm mixing my diving and pirate metaphors but hey, it's a hack about shipwrecks and have you read the stories of some of those boats??!! + +Whilst it is entirely possible for an experienced data engineer to automate the retrieval of the datasets and process through to Silver in the hack, the provided data is to allow students to 'jump in' at a point where they can continue to build their solution. + +[Challenge 0](../Student/Challenge-00.md) includes a pre-requisite to create a new workspace and lakehouse - this is required to be able to use any of the provided data (or undertake the hack in general, really). + +[Challenge 1](../Student/Challenge-01.md) is about finding data sources on the web, and Coaches are strong advised to gently nudge students with hints towards the two sources used in the reference solution (but of course, students are free to use any data source they wish). + +In [Challenge 2](../Student/Challenge-02.md), students will be required to land their data in OneLake and transform to Bronze. Instead of retrieving these data files via script or manually, students can use the provided ``Raw`` zone copies, from the 'suggested' sources. + +*"Yarr, ye done a good job finding treasure on the high seas, your Captain be proud of ye. Now, land ho that data ye-selves or, perchance take a look-see in the RAW chest. Now, swab the decks, I mean data, your Captain wants me bronze!"* + +Using these provided ``Raw`` files will allow students to focus on data wrangling and transformation as opposed to the data retrieval, removing the requirement for SLIP registration and FTP access to BOM. Of course, if they want to do it the ~~hard~~ challenging way, they can! + +In [Challenge 3](../Student/Challene-03.md), students enrich data to form a silver dataset ready for reporting. Again, if students are struggling with the data wrangling, they can use the provided ``Bronze`` zone data to focus on the enrichment, and if they are struggling with the enrichment they can use the provided ``Silver`` zone file and just write code (or a dataflow) to load to a delta table. + +All students should be able to author a dataflow to load the BOM forecast XML to a delta table. (Yes, XML, I know, but this is a hack about archaeology so...). + +*"Yarr, coaches be needing to get in character. Me sure ye can make up some more pirate-y puns on using bronze and silver. Me sure, ye find this preferable to walking the plan!"* + +At the end of [Challenge 3](../Student/Challene-03.md) students should have a Silver zone dataset of forecast and shipwrecks loaded to delta tables ready for reporting. A notebook ``Solution - Data Engineering.ipynb`` with complete ``Raw`` to ``Silver`` transformation code can be found in [Solutions](./Solutions) (this should virtually be the same code found in ``createStudentResources.py``) An example dataflow M code can be found in ``Dataflow Load IDW11160 M Code.txt`` and a Power Query Template (``Load BOM Forecasts.pqt``) in the [Solutions](./Solutions) folder. See [Solution 2](./Solution-02.md) for more details on these solutions. + + +#### Creating Student Resources + +Coaches should run the script ``createStudentResources.py`` in the [Solutions folder](./Solutions) to create a ``resources.zip`` file and share with students at the start of the event. The script will create a ``resources.zip`` containing the following files/folders: + +``` +resources.zip +├───Raw Raw data files as sourced from BOM / WAM +│ ├───BOM Bureau of Meteorology data +│ │ │ IDW11160.xml Local waters forecast at the time of running the script +│ │ └───IDM00003 BOM marine zones shapefiles +│ │ IDM00003.dbf +│ │ IDM00003.prj +│ │ IDM00003.sbn +│ │ IDM00003.sbx +│ │ IDM00003.shp +│ │ IDM00003.shp.xml +│ │ IDM00003.shx +│ │ +│ └───WAM Western Australian Museum data +│ Shipwrecks_WAM_002_WA_GDA94_Public.geojson +│ +├───Bronze Bronze zone with minimal transformation +│ marinezones.geojson BOM marine zones +│ shipwrecks.geojson WAM Shipwrecks +| +└───Silver Silver zone enriched data (ready for Power BI) + shipwrecks.json WAM Shipwrecks with relevant BOM Marine Zone (foreign key to Forecast is: AAC) +``` + +The following table provides some guidance on which files to may be of use to students based on their role: + +|Audience|Suggested Starting Data| +|--------|-----------------------| +|Data Engineers|Raw data or directly from source| +|Data Analysts|Raw data, or Bronze minimally cleaned data| +|Data Visualisation / BI Analysts|Bronze or Silver enriched data| + +These files can also be used to 'catch up' any students who may be struggling with a particular a challenge. However. all students should be encouraged to create a dataflow to ingest the raw forecasts data. + +#### Running ``createStudentResources.py`` +The script requires Python >3.4. + +1. Install Python from [https://www.python.org/downloads/](https://www.python.org/downloads/) (3.12 at time of writing) +2. In a new terminal, create a virtual environment and activate it: +``` + C:\MyWTHFolder> python -m venv venv + ``` + + ``` + C:\MyWTHFolder> .\venv\Scripts\activate.bat + (or in a Powershell shell venv/Scripts/activate) + ``` +3. Install the required package: + ``` +(venv) C:\> pip install geopandas +Collecting geopandas + ... more messages appear here ... +Successfully installed attrs-23.1.0 certifi-2023.7.22 click-8.1.7 click-plugins-1.1.1 cligj-0.7.2 colorama-0.4.6 fiona-1.9.5 geopandas-0.14.0 numpy-1.26.1 packaging-23.2 pandas-2.1.1 pyproj-3.6.1 python-dateutil-2.8.2 pytz-2023.3.post1 shapely-2.0.2 six-1.16.0 tzdata-2023.3 +``` +4. Run the script: +``` +(venv) C:\MyWTHFolder> python "/createStudentResources.py" +Downloading data.... +Downloading file from URL: https://raw.githubusercontent.com/Microsoft/WhatTheHack/067-FabricLakehouse/Student/Resources/Shipwrecks_WAM_002_WA_GDA94_Public.geojson to data/Raw/WAM\Shipwrecks_WAM_002_WA_GDA94_Public.geojson +Downloading file from URL: ftp://anonymous@ftp.bom.gov.au/anon/gen/fwo/IDW11160.xml to data/Raw/BOM\IDW11160.xml +Downloading file from URL: ftp://anonymous@ftp.bom.gov.au/anon/home/adfd/spatial/IDM00003.zip to data/Raw/BOM/IDM00003\IDM00003.zip +Extracting data/Raw/BOM/IDM00003\IDM00003.zip to data/Raw/BOM/IDM00003 +Processing raw files... +Shipwrecks... +Marine Zones... +Joining datasets... +Zipping data/resources.zip +``` +5. Deactivate the virtual environment: +``` +(venv) C:\MyWTHFolder> deactivate +C:\MyWTHFolder> +``` + +You should now have a ``resources.zip`` file in the ``data`` folder, and un-archived files in the ``data/Raw``,``data/Bronze`` and ``data/Silver`` folders. + + +### Uploading the Data + +Unzip the ``resources.zip`` file. Open the the hack Lakehouse (created as part of [Challenge 0](./Challenge-00.md)), click on the ``...`` menu next to the ``Files`` item and select ``Upload -> Upload Folder`` + +Repeat for each of the ``Raw``, ``Bronze`` and ``Silver`` folders as required. + +![](images/uploadfolder.png) + +---- + +## Azure / Fabric Requirements + +### Fabric Tenant +This hack requires students to have access to a **Microsoft Fabric tenant account with an active subscription**. These tenant requirements should be shared with stakeholders in the organization that will be providing the Fabric environment that will be used by the students (most likely the student employer). + +Students may be able to register for a [trial tenant](https://signup.microsoft.com/get-started/signup?OfferId=f6f20264-e785-4749-bd8e-884bab076de4&ali=1&products=f6f20264-e785-4749-bd8e-884bab076de4) and activate a Fabric trial, if they haven't previously done so. + +There are no specific Azure resources required for this hack, beyond a Fabric capacity (see below). + +### Enabling Microsoft Fabric + +Fabric needs to be enabled in the tenant (see [Enable Microsoft Fabric for your organization](https://learn.microsoft.com/en-us/fabric/admin/fabric-switch)) and students need to be granted permission to create Fabric resources. Depending on the host org config and policy, students could be added to an security group that has permission to create Fabric resources: + +![](https://learn.microsoft.com/en-us/fabric/admin/media/fabric-switch/fabric-switch-enabled.png) + +### Microsoft Fabric Licenses + +At the time of writing, Fabric is in preview and a trial license is available. See [Start a Fabric (Preview) trial](https://learn.microsoft.com/en-us/fabric/get-started/fabric-trial). Post-trial, a paid Fabric capacity may be required, although a small F2-F4 capacity should be sufficient for this hack. + +Overall, students must have a capacity backed workspace, through one of the following methods. +1. FT - [Fabric trial](https://learn.microsoft.com/fabric/get-started/fabric-trial) +2. F - [Fabric capacity](https://learn.microsoft.com/fabric/enterprise/buy-subscription#buy-an-azure-sku) +3. P - [Power BI Premium](https://learn.microsoft.com/power-bi/enterprise/service-admin-premium-purchase) + +See [Microsoft Fabric Licenses](https://learn.microsoft.com/en-us/fabric/enterprise/licenses) for details. + +### Microsoft Fabric Workspace + +Students will require a Microsoft Fabric enabled Workspace where they can create Fabric artefacts (Lakehouse, Dataflow, Pipeline, Notebook, Report etc). If the group is arranged into pods, it is recommended to provision one workspace per pod. If the group is working individually, it is recommended to provision one workspace per student. It is recommended that students are granted Admin role on this workspace to allow them to create and manage all artefacts. + +*Note:* for pods collaborating in a per-pod Workspace, students will also require a Power BI Pro license if they intend to publish reports to this workspace. + +See [Microsoft Fabric Workspaces](https://learn.microsoft.com/en-us/fabric/get-started/workspaces) and [Create a workspace](https://learn.microsoft.com/en-us/fabric/get-started/create-workspace) for details. + + +### Power BI Desktop + +Students will require Power BI desktop to be installed on their PC. Either the store or download version is fine. +1. [Microsoft Store](https://aka.ms/pbidesktop) See also [Learn more about the benefits of installing from the Microsoft Store including automatic updates](https://docs.microsoft.com/power-bi/fundamentals/desktop-get-the-desktop#install-as-an-app-from-the-microsoft-store). + or +2. [Download Center](https://www.microsoft.com/en-us/download/details.aspx?id=58494) Ensure you have the latest version of downloaded. + +## Repository Contents + +- `./Coach` + - Coach's Guide and related files +- `./Coach/Solutions` + - Solution files with completed example answers to a challenge +- `./Student` + - Student's Challenge Guide +- `./Student/Resources` + - Resources (data files) required by the students are generated and not contained in this folder. See **Student Resources** above for details. + +## Other Fabric What The Hacks + +These What The Hacks are currently in development and will be released soon: +- Fabric Data Mesh - A more architecturally focused hack covering Data Mesh, Medallion Architecture, and Fabric +- Fabric Realtime - A hack focused on realtime data processing with Fabric diff --git a/067-FabricLakehouse/Coach/SlidesImages/De_BATAVIA_voor_de_film_onderweg_als_de_NIEUW_HOORN.jpg b/067-FabricLakehouse/Coach/SlidesImages/De_BATAVIA_voor_de_film_onderweg_als_de_NIEUW_HOORN.jpg new file mode 100644 index 0000000000..c7dfefd6f4 Binary files /dev/null and b/067-FabricLakehouse/Coach/SlidesImages/De_BATAVIA_voor_de_film_onderweg_als_de_NIEUW_HOORN.jpg differ diff --git a/067-FabricLakehouse/Coach/SlidesImages/clownfish.jpg b/067-FabricLakehouse/Coach/SlidesImages/clownfish.jpg new file mode 100644 index 0000000000..9a239ab8a4 Binary files /dev/null and b/067-FabricLakehouse/Coach/SlidesImages/clownfish.jpg differ diff --git a/067-FabricLakehouse/Coach/SlidesImages/pexels-arthouse-studio-4347768.jpg b/067-FabricLakehouse/Coach/SlidesImages/pexels-arthouse-studio-4347768.jpg new file mode 100644 index 0000000000..a101d052b9 Binary files /dev/null and b/067-FabricLakehouse/Coach/SlidesImages/pexels-arthouse-studio-4347768.jpg differ diff --git a/067-FabricLakehouse/Coach/SlidesImages/pexels-leonardo-lamas-7001617.jpg b/067-FabricLakehouse/Coach/SlidesImages/pexels-leonardo-lamas-7001617.jpg new file mode 100644 index 0000000000..5aad99b355 Binary files /dev/null and b/067-FabricLakehouse/Coach/SlidesImages/pexels-leonardo-lamas-7001617.jpg differ diff --git a/067-FabricLakehouse/Coach/Solution-00.md b/067-FabricLakehouse/Coach/Solution-00.md new file mode 100644 index 0000000000..c393493416 --- /dev/null +++ b/067-FabricLakehouse/Coach/Solution-00.md @@ -0,0 +1,24 @@ +# Challenge 00 - Prerequisites - Grab your fins and a full tank! - Coach's Guide + +**[Home](./README.md)** - [Next Solution >](./Solution-01.md) + +## Notes & Guidance + +Please make sure that the students review the [introduction](../README.md) and [Challenge 0 - Prerequisites - Ready, Set, GO! - Student's Guide](../Student/Challenge-00.md) ahead of time. Also ensure you have read the prerequisites section of the [Coach's Guide](./README.md). + +Students will need access to a Fabric enabled workspace and Power BI desktop. Again, see the pre-reqs in the [Coach's Guide](./README.md) for more details. + +Students will need to have created a Lakehouse in their workspace either ahead of time, or on the day. This is covered in the [Create a Lakehouse](https://learn.microsoft.com/en-us/fabric/data-engineering/create-lakehouse) tutorial on Learn for those not familiar. + +You should provide the students with the ``resources.zip`` file and ensure they have uploaded each of the contained folders to their Lakehouse. Unfortunately, this has to be done one folder at a time so allow a small amount of time to complete. See the student resources section of the [Coach's Guide](./README.md) for details on creating the ``resources.zip`` file. + +## Gotchas + +Overall, this challenge is designed to level set student environments and ensure they are ready to undertake the rest of the hack. Some things to watch out for: + +1. **Fabric not enabled in the tenant.** This is a common issue and needs validating/resolving when planning the hack. During the organisation stage, ensure that tenant(s) to be used by students have been enabled for Fabric, either globally or to a specific group of users (that the student is a member of). See Enabling Microsoft Fabric in the [README](./README.md) +2. **No Fabric capacity.** A Fabric capacity is required. A small SKU is acceptable (F2-F4). A trial developer subscription may be of use as well. See [README](./README.md) +3. **Workspace not Fabric enabled, or no permissions to create new workspace.** Ideally, a new workspace should be created for each student ahead of time either by their tenant admin or, if they have permissions, themselves. Students require at least Contributor role, although workspace Admin is suggested. See [learn.microsoft.com/en-us/fabric/get-started/workspaces](https://learn.microsoft.com/en-us/fabric/get-started/workspaces) +4. **Uploading Data to Wrong Location** You should ensure the students upload the data to the correct location (i.e., Files/Raw, Files/Bronze etc) not Files/data/Raw etc. This is a common mistake and can be confusing for students especially if you later provide them with the included code hints/solutions. + + diff --git a/067-FabricLakehouse/Coach/Solution-01.md b/067-FabricLakehouse/Coach/Solution-01.md new file mode 100644 index 0000000000..19acfde83f --- /dev/null +++ b/067-FabricLakehouse/Coach/Solution-01.md @@ -0,0 +1,56 @@ +# Challenge 01 - Finding Nemo - Coach's Guide + +[< Previous Solution](./Solution-00.md) - **[Home](./README.md)** - [Next Solution >](./Solution-02.md) + +## Notes & Guidance + +This first challenge is all about finding the data but not importing it (yet). The output is a list of datasets that meet the requirements, a strategy for ingesting / processing and a selection of the "best" tool - notebook, dataflow etc. Actual development starts in challenge 2. + +For this challenge, the students will be searching for suitable data sources online. You should ensure that they are aware of the following: + +- Licensing +- Copyright + +This object of this challenge is to get the students to think about: + +- the data they need to meet the requirements +- what sources are available +- how it is licensed +- how they can land this data automatically in OneLake + +Whilst there is no formal output of this challenge, you should ensure that the students have made a note of the datasets they have found, and how they plan to ingest them as this will be useful in the next challenge. + +## Solution + +### (Strongly) Recommended Datasets + +The example solutions have been built using Australian Bureau of Meteorology and Western Australian Museum datasets. These are the (strongly) recommended sources for the hack. However, students are free to use any datasets they like, as long as they meet the requirements to spatially locate a wreck and weather conditions at that point. Substitute datasets need to be licensed appropriately, and if students do decided to attempt creating a custom solution, you should ensure they are aware of the time constraints and the need to move on to the next challenge. You may also need to provide deeper technical guidance if they are not familiar with the tools but hey, if they want to try, let them! They can always revert to the provided solutions if they get stuck. + +These two BOM datasets comprise forecasts for marine zones (with a textual zone key) and geo-coded marine zones (to allow spatially locating shipwrecks to a zone). + +- BOM FTP data services: http://www.bom.gov.au/catalogue/data-feeds.shtml and http://www.bom.gov.au/catalogue/anon-ftp.shtml + + - ``IDW11160`` - Coastal Waters Forecast - All Districts (WA) + - ``IDM000003`` - Marine Zones - http://reg.bom.gov.au/catalogue/spatialdata.pdf + + +This dataset contains wreck details (date, name, description etc) in GeoJSON format, allowing joining to ``IDM000003`` and by extension, ``IDW11160``. + +- WA Museum + - ``WAM-002`` https://catalogue.data.wa.gov.au/dataset/shipwrecks (requires a free SLIP account and is CC BY 4.0) + +### More Advanced Datasets + +More advanced students might like to include climate (temperature and wave) models from [ECMWF Open Data](https://planetarycomputer.microsoft.com/dataset/ecmwf-forecast) available via the Microsoft Planetary Computer. See https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/ + +### Common Issues / Pitfalls + +- Students may struggle to find suitable datasets. Guide those who are a bit lost at sea with some hints such as + - _"Who would be interested in documenting the history of shipwrecks?"_ + - A: Museums! + - _"And are there any in Western Australia?"_ + - A: WA Museum (and Shipwreck Galleries in particular) + - _"Is there an open data portal for all Australian government data (Federal and State)?"_ + - A: [data.gov.au](https://data.gov.au/) + - "Is there a government agency that might have data on weather conditions?" + - A: [Bureau of Meteorology](http://www.bom.gov.au/) diff --git a/067-FabricLakehouse/Coach/Solution-02.md b/067-FabricLakehouse/Coach/Solution-02.md new file mode 100644 index 0000000000..89240f47a4 --- /dev/null +++ b/067-FabricLakehouse/Coach/Solution-02.md @@ -0,0 +1,48 @@ +# Challenge 02 - Land Ho! - Coach's Guide + +[< Previous Solution](./Solution-01.md) - **[Home](./README.md)** - [Next Solution >](./Solution-03.md) + +## Notes & Guidance + +This challenge implements the design developed in Challenge 1, landing data in a "raw" format and cleaning to bronze, ready to further transform in [the next Challenge](./Solution-03.md). + +## Outcome +At the end of this challenge, students should have landed their data in OneLake and cleaned it to bronze. The data should be available in the Lakehouse as GeoJSON files for shipwrecks and marine zones, and as an XML file for forecasts. + +## Solution + +Students may wish: + +- to download the data manually and upload to OneLake +- to use a notebook, a dataflow or a combination of both to retrieve and land the data +- to use the provided raw data files + +Any of these are acceptable. + +Once the raw data has been landed, students will need to clean the data to bronze. The example solution uses a notebook to perform this step for shipwrecks and marine zones as this is the most applicable tool for processing spatial data. Since parquet does not support ``geometry`` types, shipwrecks and marine zones are stored as GeoJSON in bronze. + +> **A Note on Forecasts** +>Some students may wish to transform forecasts data from XML to json, csv, etc. and write to bronze files with a dataflow. They may then want to process this file to silver using Spark as part of Challenge 3. Whilst not strictly necessary it is perfectly acceptable to do so. However, the example dataflow solution in [Challenge 3](./Solution-03.md) processes forecasts directly to silver skipping this intermediate step. + +#### Example Notebook - Data Exploration + +An important part of data engineering is understanding the data. Students should be encouraged to explore the data and a sample notebook is provided in the [Solutions](./Solutions) folder ``Data Exploration.ipynb``. + +Coaches can use this notebook to demonstrate how to explore the data, as prompts to help guide students, and to help students with the steps required to initially clean the data to bronze. + +#### Example Notebook - Shipwrecks and Marine Zones Data Engineering + +Challenge 2 and 3 solutions for shipwrecks and marine zones are contained in the notebook [Solution - Data Engineering.ipynb](./Solutions/Solution%20-%20Data%20Engineering.ipynb). + + +The notebook contains notes on the steps required for this challenge (and challenge 3). Coaches should step through each code cell to become familiar with the overall code, and be able to use snippets of code from this notebook to help students. + +#### Uploading the Notebooks + +Coaches should upload notebooks to their workspace following [How to use Microsoft Fabric notebooks](https://learn.microsoft.com/en-us/fabric/data-engineering/how-to-use-notebook#import-existing-notebooks). + +#### Advanced Students + +More (very..) advanced students might like to include climate (temperature and wave) models from [ECMWF Open Data](https://planetarycomputer.microsoft.com/dataset/ecmwf-forecast) available via the Microsoft Planetary Computer. Coaches + +An example notebook is included in the [Solutions](./Solutions) folder ``Loading Planetary Computer Climate Prediction Models.ipynb``. diff --git a/067-FabricLakehouse/Coach/Solution-03.md b/067-FabricLakehouse/Coach/Solution-03.md new file mode 100644 index 0000000000..72d912f875 --- /dev/null +++ b/067-FabricLakehouse/Coach/Solution-03.md @@ -0,0 +1,141 @@ +# Challenge 03 - Swab the Decks! - Coach's Guide + +[< Previous Solution](./Solution-02.md) - **[Home](./README.md)** - [Next Solution >](./Solution-04.md) + +## Notes & Guidance + +Challenge Three is about cleaning and loading data from ``Bronze`` files to ``Silver`` delta tables in preparation for reporting. + +The students should be encouraged to explore both notebooks and data flows (for example, processing BOM forecast XML is easy in a dataflow, but the WAM-002 data is better suited to a notebook). + +Overall, the method used by the students are very much a design choice by each. + +## Solutions + +Solutions are contained in the [Solutions](./Solutions) folder: + +__Dataflow2__ + +- ``Load BOM Forecasts.pqt`` - dataflow template - M code for the dataflow is below. + +__Notebooks__ + +- ``Solution - Data Engineering.ipynb`` - this notebook contains a solution for both [Challenge 2](./Solution-02.md) and [Challenge 3](./Solution-03.md) and should be read in conjunction with ``Data Exploration.ipynb``. + +- ``Data Exploration.ipynb`` - shows one way to explore the data and the contains the steps required to clean the data to bronze, and to enrich to silver. Coaches should step through each code cell to become familiar with the overall code, and be able to use snippets of code from this notebook to help students. + +- ``Loading Planetary Computer Climate Prediction Models.ipynb`` - super bonus boss level - loads ECMWF climate models from the Microsoft Planetary Computer for students who want to go further. **Warning** this is hard... + +__Misc__ + +- ``Cleanup.ipynb`` - cleans up OneLake tables and files +- ``troubleshooting/Cancel-Dataflow.ps1`` - a PowerShell script to cancel a dataflow (or at least mark the metadata as cancelled). Rarely a dataflow may not complete correctly. This script can be used to help cancel the dataflow so it can be re-run. + +--- + +## Dataflow + +Coaches should import the dataflow template ``Load BOM Forecasts.pqt`` from the solutions folder for an example of loading the ``IDW11160.xml`` file into a Lakehouse table. + +**Note: this dataflow assumes the source file is ``Files/Raw/BOM/IDW11160.xml`` the Lakehouse.** + +### Importing the Dataflow + +Coaches should review [Move queries from Dataflow Gen1 to Dataflow Gen2](https://learn.microsoft.com/en-us/fabric/data-factory/move-dataflow-gen1-to-dataflow-gen2) for an overview of the process. + +1. Navigate to your Lakehouse. From the URL, copy the ``workspaceId`` (the part after ``/group/`` and before ``/lakehouses`` in yellow below) and the ``lakehouseId`` (the part after ``/lakehouses/`` in red below). +![](./images/workspacelakehouseid.png) +1. Navigate back to your workspace, and then create a new **Dataflow Gen 2** +![](images/dataflowgen2.png) +**Important! Make sure you select Dataflow Gen 2 from the Data Factory section.** +1. Import the ``Load BOM Forecasts.pqt`` template +![](images/importdataflow.png) +1. You should now see a new dataflow. +![](images/newdataflow.png) +1. Set the credentials by clicking on the ``Configure connection`` button and click ``Connect`` +![](images/dataflowcreds.png) +1. Update the parameters with the ``workspaceId`` and ``lakehouseId`` from step 1. +1. Set the data destination for the Forecast query. Click on the query and set the destination to ``Lakehouse`` +![](images/destination.png) +1. Navigate to the correct workspace and lakehouse. +1. Verify the column mappings (you may have to change type ``any`` to the correct type (or ``text``) +1. Finally Publish the dataflow. The dataflow should automatically run and load the data into ``Forecast`` table in the Lakehouse. +![](images/forecasts.png) + + +### Manually creating the dataflow + +The dataflow can also be manually created. + +1. First, create a new dataflow step 1-2 above). +1. Next, add two parameters for ``workspaceId`` and ``lakehouseId`` (see [Parameters - Power Query](https://learn.microsoft.com/en-us/power-query/power-query-query-parameters#creating-a-parameter). +1. Finally, add the following code as to a blank query (see [Share a Query](https://learn.microsoft.com/en-us/power-query/share-query#copy-the-m-code). + +Set the credentials and publish the dataflow as above. + +```M +let + Source = Lakehouse.Contents([]), + Navigation = Source{[workspaceId = workspaceId]}[Data], + #"Navigation 1" = Navigation{[lakehouseId = lakehouseId]}[Data], + #"Navigation 2" = #"Navigation 1"{[Id = "Files", ItemKind = "Folder"]}[Data], + #"Navigation 3" = #"Navigation 2"{[Name = "Raw"]}[Content], + #"Navigation 4" = #"Navigation 3"{[Name = "BOM"]}[Content], + #"Navigation 5" = #"Navigation 4"{[Name = "IDW11160.xml"]}[Content], + #"Imported XML" = Xml.Tables(#"Navigation 5"), + #"Navigation 6" = #"Imported XML"{0}[forecast], + #"Navigation 7" = #"Navigation 6"{0}[area], + #"Changed column type 1" = Table.TransformColumnTypes(#"Navigation 7", { {"Attribute:aac", type text}, {"Attribute:description", type text}, {"Attribute:type", type text}, {"Attribute:parent-aac", type text} }), + #"Expanded forecast-period" = Table.ExpandTableColumn(#"Changed column type 1", "forecast-period", {"text", "Attribute:start-time-local", "Attribute:end-time-local", "Attribute:start-time-utc", "Attribute:end-time-utc"}, {"text", "Attribute:start-time-local", "Attribute:end-time-local", "Attribute:start-time-utc", "Attribute:end-time-utc"}), + #"Expanded text" = Table.ExpandTableColumn(#"Expanded forecast-period", "text", {"Element:Text", "Attribute:type"}, {"Element:Text", "Attribute:type.1"}), + #"Pivoted column" = Table.Pivot(Table.TransformColumnTypes(#"Expanded text", {{"Attribute:type.1", type text}}), List.Distinct(Table.TransformColumnTypes(#"Expanded text", {{"Attribute:type.1", type text}})[#"Attribute:type.1"]), "Attribute:type.1", "Element:Text"), + #"Removed columns" = Table.RemoveColumns(#"Pivoted column", {"Attribute:start-time-local", "Attribute:end-time-local"}), + #"Renamed columns" = Table.RenameColumns(#"Removed columns", { {"Attribute:start-time-utc", "StartTime"}, {"Attribute:end-time-utc", "EndTime"}, {"Attribute:aac", "AAC"}, {"Attribute:description", "Description"}, {"Attribute:type", "Type"}, {"Attribute:parent-aac", "ParentAAC"}, {"synoptic_situation", "SynopticSituation"}, {"preamble", "Preamble"}, {"warning_summary_footer", "WarningSummaryFooter"}, {"product_footer", "ProductFooter"}, {"postamble", "Postamble"}, {"forecast_winds", "ForecastWinds"}, {"forecast_seas", "ForecastSeas"}, {"forecast_swell1", "ForecastSwell"}, {"forecast_weather", "ForecastWeather"} }) +in + #"Renamed columns", + #"Changed column type" = Table.TransformColumnTypes(Source, { {"StartTime", type datetime}, {"EndTime", type datetime}, {"SynopticSituation", type text}, {"Preamble", type text}, {"WarningSummaryFooter", type text}, {"ProductFooter", type text}, {"Postamble", type text}, {"ForecastWinds", type text}, {"ForecastSeas", type text}, {"ForecastSwell", type text}, {"ForecastWeather", type text}, {"forecast_swell2", type text} }) +in + #"Changed column type" +``` + +### Troubleshooting + +Between the time of writing and the hack, there may have been changes made to the BOM xml file. Coaches are strongly advised to **download the latest forecast file and validate the dataflow** before the hack. Commonly, we have seen columns not included in some forecast files, or forecasts within a file which will cause the code above to fail. + +We welcome contributions to help make this M code a little more robust. + +## Building the Semantic Data Model + +Fabric will create a default semantic model once the Delta tables have been written to OneLake that can be used for reporting, but coaches may wish to encourage students to expand this model (or create a custom model). + + +Students familiar with Power BI will most likely want to use Power BI Desktop to build their semantic model. This is not the way. Direct Lake semantic models are created and edited in the service, and coaches should familiarise themselves with the model editor + +To launch the model editor, navigate to your workspace, and then select the Lakehouse. In the Lakehouse view, switch the the ``SQL Analytics Endpoint.`` then select the ``Model`` tab. + +![](images/sqlanalysisendpoint.png) + +The model window will open. + +![](images/modelview.png) + +From here, relationships can be defined, measures created etc as per standard Power BI modelling. + +Refer to [Default Power BI semantic models in Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/data-warehouse/semantic-models) + +### Optional - Gold Semantic Model + +More advanced students (or those with a data modelling background) may wish to create a dimensional model using Spark, data flows or other tooling, instead of using the base tables. + +Some suggestions: + +- Create a ``Date`` dimension. +- Model ``Shipwrecks`` as dimensions for ``Country``, ``Port``,``ShipClass``, ``District`` etc +- Model ``Forecasts`` as a dimension for ``ForecastType`` and ``ForecastPeriod`` +- Model ``Wrecks`` as a fact table - wreck date, location etc + +> **Note:** Creating a dimensional model is not required for this challenge, but is a good way to introduce students to dimensional modelling in Fabric. Coaches with a Power BI background will be familiar with real-world reporting from data which has not been dimensionally modelled, and may wish to discuss the benefits of dimensional modelling with students. The focus of the hack is on the end-to-end Fabric experience, so coaches should not over emphasise building a 'correct' model for this small dataset. + +At the time of writing, a second Fabric hack is being developed which will focus on datawarehousing and dimensional modelling in Fabric. We welcome contributions to this hack via the GitHub repo. + +[< Previous Solution](./Solution-02.md) - **[Home](./README.md)** - [Next Solution >](./Solution-04.md) diff --git a/067-FabricLakehouse/Coach/Solution-04.md b/067-FabricLakehouse/Coach/Solution-04.md new file mode 100644 index 0000000000..1bec3ff963 --- /dev/null +++ b/067-FabricLakehouse/Coach/Solution-04.md @@ -0,0 +1,15 @@ +# Challenge 04 - Make a Splash! - Coach's Guide + +[< Previous Solution](./Solution-03.md) - **[Home](./README.md)** - [Next Solution >](./Solution-05.md) + +## Notes & Guidance + +This challenge is to visualise the data. Power BI is the obvious choice, but if a group wishes to build a dashboard in Excel, or use Power Apps, or React then that is fine too. + +Emphasis should be placed on the data story and meeting the requirements. + +A good data story will have a clear narrative, and will be easy to follow. It will also be visually appealing, address the specific needs of the audience, and will make good use of the data. Students should be encouraged to use other media (whilst keeping within any licensing terms) such as images, to enhance their story. + +## Solution + +Data stories are subjective, and there are many ways to visualise the data. As such an example solution is not (yet) provided. If you are a PBI visualization wizard, please feel free to contribute! diff --git a/067-FabricLakehouse/Coach/Solution-05.md b/067-FabricLakehouse/Coach/Solution-05.md new file mode 100644 index 0000000000..b201f19485 --- /dev/null +++ b/067-FabricLakehouse/Coach/Solution-05.md @@ -0,0 +1,21 @@ +# Challenge 05 - Giant Stride - Coach's Guide + +[< Previous Solution](./Solution-04.md) - **[Home](./README.md)** + +## Notes & Guidance + +This challenge is about showing value to the customer and reflecting on the day. Although it might be tempting to drop this challenge (especially if time is tight), it's important for the teams to present their work so please try to reserve time to include this activity. + +## Coach's Role + +How's your acting? BAFTA worthy? Oscar worthy? Well, now's your chance to shine. You will play the part of the customer. Make up a character (CDO, Dive Shop Manager, Salty old sea dog yarr) and make it a fun retrospective. How's your pirate accent? + +Allocate time for each team to present their work. Encourage the teams to present their data stories, and to reflect on the day. What went well? What could have gone better? What did they learn? What would they do differently next time? + +You might like to award small prizes for the best team, the best presentation, or the best pirate accent, but this challenge is not about winning or losing; it's about reflecting on the day and having fun. It's all about the bubbles we blew along the way. + +## Finally + +Thanks for being a coach. We hope you enjoy delivering this hack as much as we enjoyed writing it. We value your feedback (and contributions) and you can always drop by our GitHub ~~dive shop~~ repo. + +___Avast! Ye scurvy dogs! Yo ho ho and a bottle of rum!___ diff --git a/067-FabricLakehouse/Coach/Solutions/.gitkeep b/067-FabricLakehouse/Coach/Solutions/.gitkeep new file mode 100644 index 0000000000..e69de29bb2 diff --git a/067-FabricLakehouse/Coach/Solutions/Data Exploration.ipynb b/067-FabricLakehouse/Coach/Solutions/Data Exploration.ipynb new file mode 100644 index 0000000000..6e69b6f39c --- /dev/null +++ b/067-FabricLakehouse/Coach/Solutions/Data Exploration.ipynb @@ -0,0 +1,849 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "319d31ed-b479-46c8-b69f-9559eff77468", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "# Data Exploration\n", + "This notebook is to provide the coach with some ideas on exploring the data" + ] + }, + { + "cell_type": "markdown", + "id": "b0a1da9d-1fd3-4e1c-8fbc-24b4dc8ef527", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Dependencies" + ] + }, + { + "cell_type": "markdown", + "id": "c5ed84a4-b082-4f7a-82fd-7607d72f160d", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Some handy libraries\n", + "- ``geopandas`` https://geopandas.org/ to work with geojson files\n", + "- ``contextily`` https://contextily.readthedocs.io/ to render map tile backgrounds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab1bd6c7-fe46-430b-b4f4-129452a27425", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install geopandas contextily" + ] + }, + { + "cell_type": "markdown", + "id": "90f919a8-f4a4-4a6e-81a9-caa1fe503cff", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Locations of our raw files in OneLake, either downloaded or from ``resources.zip``. See ``Solution - Data Engineering`` notebook for details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49b84853-5b62-41be-8c6f-9d930d134534", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import geopandas as gp\n", + "\n", + "rawFilesFolder = \"/lakehouse/default/Files/Raw/\"\n", + "\n", + "marineZonesRawFolder = f\"{rawFilesFolder}BOM/IDM00003\"\n", + "marineZonesRawFile = f\"{marineZonesRawFolder}/IDM00003.shp\"\n", + "\n", + "shipwrecksRawFolder = f\"{rawFilesFolder}WAM\"\n", + "shipwrecksRawFile = f\"{shipwrecksRawFolder}/Shipwrecks_WAM_002_WA_GDA94_Public.geojson\"" + ] + }, + { + "cell_type": "markdown", + "id": "0adea955-34c6-499a-8abd-4c08f15ed6ef", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Basic Exploration\n", + "First let's load our data from Raw." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "972dc142-b40b-42f0-b34f-265bc202fc3f", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_shipwrecks = gp.read_file(shipwrecksRawFile)\n", + "df_marineZones = gp.read_file(marineZonesRawFile)" + ] + }, + { + "cell_type": "markdown", + "id": "1d65303e-f1bd-4dd8-9309-c39f79e735d2", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Let's look at the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11aaa6ba-d6cd-4ee8-bc49-4da12757e944", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_shipwrecks.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5cd5edf-c1af-4391-8c8f-689a377f89ad", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_marineZones.head()" + ] + }, + { + "cell_type": "markdown", + "id": "50eadb86-787c-4ca5-a8eb-5fc3ccfd0b84", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Spatial data uses a Co-ordinate Reference System to define co-ordinate space to geo space translation, let's check ours" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4efbe4d-4122-4e58-81ee-8c0147ec4764", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_shipwrecks.crs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26ba44d0-0e7b-4ddc-85e9-1ae4de920a3b", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_marineZones.crs" + ] + }, + { + "cell_type": "markdown", + "id": "9efdd2a0-4af0-4450-9528-05f0ca330505", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "EPSG:4326 is WGS84, World Geodetic System 1984 as used in GPS. EPSG:4283 is GDA94, the Geocentric Datum of Australia 1994\n", + "\n", + "Let's normalise these to EPSG:3857 commonly used by web mapping tools (aka Web Mercator)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38c7f213-56e9-4bec-8e82-2b7d54d1302f", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_shipwrecks = df_shipwrecks.to_crs('epsg:3857')\n", + "df_marineZones = df_marineZones.to_crs('epsg:3857')\n", + "print(df_shipwrecks.crs)\n", + "print(df_marineZones.crs)" + ] + }, + { + "cell_type": "markdown", + "id": "45cd85c3-6ee5-45bd-b585-bc21a3279bcf", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Basic Plots\n", + "We're working with spatial data, so let's make some plots." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43397606-075e-4138-8af6-5206e48502ee", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_shipwrecks.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2059761-6459-4bbe-8421-276231233dec", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_marineZones.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "c5c7a5c8-6795-48af-b149-6f68340a382b", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Let's make them a little more fancy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d617154-bafc-413d-bd9c-fad983b097b9", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_marineZones.plot(figsize=(10,10), alpha=0.3, edgecolor=\"k\", column='DIST_NAME', categorical=True, legend=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e627774e-fac3-489a-b2a6-63c3423701ba", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "We can overlay our ``shipwrecks``:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "332e7be7-547c-430d-8b14-2ef441182a0b", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "ax = df_marineZones.plot(figsize=(10,10), alpha=0.3, edgecolor=\"k\", column='DIST_NAME', categorical=True, legend=False)\n", + "df_shipwrecks.plot(ax=ax, color='r')" + ] + }, + { + "cell_type": "markdown", + "id": "09ccf0e6-84ad-44d6-aca2-a61f96c0c795", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "We're only interested in Western Australian shipwrecks. We could clip the ``marineZones`` to the ``bounds`` of the ``shipwrecks`` but we saw earlier, that ``marineZones`` contains a ``STATE_CODE`` column so let's use that" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82b24045-67a6-46ce-96a6-0c0a79972ea9", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_marineZones = df_marineZones[df_marineZones.STATE_CODE == \"WA\"]" + ] + }, + { + "cell_type": "markdown", + "id": "a444527f-c604-43ac-95d3-2e019089b7c5", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Let's also colour by marine zone ``DIST_NAME`` (district name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5e2ac1d-b9ed-42b4-b1ca-8a54ca5b10a9", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "ax = df_marineZones.plot(figsize=(10,10), alpha=0.3, edgecolor=\"k\", column='DIST_NAME', categorical=True, legend=False)\n", + "df_shipwrecks.plot(ax=ax, color='r')" + ] + }, + { + "cell_type": "markdown", + "id": "8b76e901-4215-4b0c-b61b-382976917277", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Nice." + ] + }, + { + "cell_type": "markdown", + "id": "ea36b14a-a72b-4af6-aba3-9cff72240375", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Let's add a base layer using ``contextily``" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e28fc560-be21-4a80-b363-dff367cee574", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import contextily as cx\n", + "\n", + "ax = df_marineZones.plot(figsize=(25,25), alpha=0.3, edgecolor=\"k\", column='DIST_NAME', categorical=True, legend=False)\n", + "df_shipwrecks.plot(ax=ax, color='r')\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "id": "74e938f9-01f3-4f77-bdc9-970fc77af0a6", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "We can already see some wrecks don't fall within a marine zone (top left - Cocos Keeling Islands )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34c4c1b4-89ca-4571-bde8-7639697ef21d", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_shipwrecks[df_shipwrecks.long == df_shipwrecks['long'].min()]" + ] + }, + { + "cell_type": "markdown", + "id": "560a2034-70c8-4479-97f4-1392fb60c219", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "We need to do a spatial anti-join to find all those shipwrecks outside of a marinezone. Unfortunately, ``geopandas`` doesn't yet support anti-joins, but we can fake it with a left outer join and a filter on the geometry from the right side. We need to clone the original right side geometry, as this is dropped during the join." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6578b3e-6344-4704-9352-c286a33ee822", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "#Deep copy our marineZones and duplicate the geometry\n", + "df_marineZones_tmp = df_marineZones.copy()\n", + "df_marineZones_tmp[\"right_geometry\"] = df_marineZones_tmp[\"geometry\"]" + ] + }, + { + "cell_type": "markdown", + "id": "3a8ef488-a468-4f6c-a173-086ed02c3e7a", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Now we can use ``sjoin`` to spatially join our data, then filter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb253c43-edf6-4de1-95f3-118dbeefe2c2", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_joined = df_shipwrecks.sjoin(df_marineZones_tmp, how=\"left\")\n", + "df_nozone = df_joined.query(\"index_right != index_right\") # fake anti-join" + ] + }, + { + "cell_type": "markdown", + "id": "09f31594-79f5-4354-9c9b-7a428461b387", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Let's plot this. Red are wrecks with no marine zone." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29c670a2-0338-43ee-9643-5fc85b4e903c", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "ax = df_marineZones.plot(figsize=(25,25), alpha=0.3, edgecolor=\"k\", column='DIST_NAME', categorical=True, legend=False)\n", + "df_joined.plot(ax=ax, color='b', alpha=0.3)\n", + "df_nozone.plot(ax=ax, color='r', alpha=1.0)\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "id": "acb2791b-7f08-4d13-87dd-15d67e972902", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Let's zoom in and look at an example, the SS Omeo, just off the shore in Perth (it's a great snorkel site, you can literally walk off the beach and be on the wreck)\n", + "\n", + "Perth is marine zone WA_MW015, let's filter and plot.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ea8e0b2-acea-4d18-af1c-ead22a03fe95", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "#The Omeo and Perth maritime zone\n", + "df_omeo = df_shipwrecks[df_shipwrecks.name=='Omeo']\n", + "df_perth = df_marineZones[df_marineZones.AAC == 'WA_MW015']\n", + "\n", + "#Bounding box of the Omeo\n", + "xmin, ymin, xmax, ymax = df_omeo.total_bounds\n", + "# Padding - 200m\n", + "pad=200\n", + "\n", + "#Plot\n", + "ax = df_omeo.plot(figsize=(5,5),color='r')\n", + "df_perth.plot(ax=ax)\n", + "\n", + "#Now set out plot limits\n", + "ax.set_xlim(xmin-pad, xmax+pad)\n", + "ax.set_ylim(ymin-pad, ymax+pad)\n", + "\n", + "#Add in our basemap\n", + "cx.add_basemap(ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "55768457-4550-42e8-888a-2ecc8eda7aa3", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "So we can see the marine zone doesn't follow the coastline, and the SS Omeo falls outside of this geo. We can't do a simple ``contains`` or ``intersects`` spatial join to place our wrecks in their relevant marine zone, we need a different kind of join. \n", + "\n", + " ``geopandas`` supports nearest joins so we can look for the zone closest to each wreck, and remove any that are arbitrarily distant." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73c94870-106a-4700-a241-8304bd865147", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_joined = df_shipwrecks.sjoin_nearest(df_marineZones, how=\"left\", distance_col=\"distance\").query(\"distance < 5000\")\n", + "\n", + "ax = df_marineZones.plot(figsize=(10,10), alpha=0.3, edgecolor=\"k\", column='DIST_NAME', categorical=True, legend=False)\n", + "df_joined.plot(ax=ax, color='r', alpha=0.5)\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "id": "767072c8-eb72-41c3-af93-17828d4d8b04", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Let's just make sure all our wrecks have a zone as we'll be using this as a foreign key to Forecasts when we come to build our reports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19b99649-b849-4443-93ea-32f3c60645c7", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_joined[df_joined['AAC'].isnull()]\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa0df079-0213-49cc-ae21-32dc02287a21", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "We can now apply our new found knowledge to load, clean and write our data - see ``Solution - Data Engineering`` notebook for details." + ] + } + ], + "metadata": { + "kernel_info": { + "name": "synapse_pyspark" + }, + "kernelspec": { + "display_name": "Synapse PySpark", + "language": "Python", + "name": "synapse_pyspark" + }, + "language_info": { + "name": "python" + }, + "microsoft": { + "host": {}, + "language": "python" + }, + "notebook_environment": {}, + "nteract": { + "version": "nteract-front-end@1.0.0" + }, + "save_output": true, + "spark_compute": { + "compute_id": "/trident/default", + "session_options": { + "conf": {}, + "enableDebugMode": false + } + }, + "synapse_widget": { + "state": {}, + "version": "0.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/067-FabricLakehouse/Coach/Solutions/Load BOM Forecasts.pqt b/067-FabricLakehouse/Coach/Solutions/Load BOM Forecasts.pqt new file mode 100644 index 0000000000..cecca0a59e Binary files /dev/null and b/067-FabricLakehouse/Coach/Solutions/Load BOM Forecasts.pqt differ diff --git a/067-FabricLakehouse/Coach/Solutions/Loading Planetary Computer Climate Prediction Models.ipynb b/067-FabricLakehouse/Coach/Solutions/Loading Planetary Computer Climate Prediction Models.ipynb new file mode 100644 index 0000000000..51dcf89f6d --- /dev/null +++ b/067-FabricLakehouse/Coach/Solutions/Loading Planetary Computer Climate Prediction Models.ipynb @@ -0,0 +1,666 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0ff8866e-b39f-4e6e-9c29-6aed455662df", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "# Loading Weather Data\n", + "### Loading Forecasts data from Microsoft Planetary Computer Catalogue\n", + " \n", + "ECMWF Open Data (real-time) From Microsoft Planetary Computer Dataset.\n", + "\n", + "https://planetarycomputer.microsoft.com/dataset/ecmwf-forecast#overview\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "6edc1d1b-282b-4f67-82e6-29d9f575482d", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Install the eecodes C library and cartopy native (C/C++) dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afe93b94-2dac-4842-8e37-610e6f0e74b8", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "%conda install -c conda-forge eccodes cartopy" + ] + }, + { + "cell_type": "markdown", + "id": "43fd5dd5-373e-4f66-b1aa-fdae84a496e6", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Install other dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ee20845-ca81-47a9-b61d-d967c3b9c458", + "metadata": { + "jupyter": { + "outputs_hidden": true, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "%pip install cfgrib eccodes rioxarray pystac_client planetary-computer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "565eeaf7-a349-46cf-ba19-553030ae6f6c", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import urllib.request\n", + "import eccodes\n", + "import cfgrib\n", + "import rioxarray\n", + "import xarray as xr\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "\n", + "import pyspark.sql.functions as f\n" + ] + }, + { + "cell_type": "markdown", + "id": "75bbc2cd-56d0-40b7-a405-ef73be2ba870", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "550fc40e-1458-4a26-ade6-69cb311017e7", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Query the STAC Planetary Computer data catalogue for ECMWF Forecast data.\n", + "\n", + "https://github.com/stac-api-extensions/query\n", + "\n", + "https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/\n", + "\n", + "Find ``enfo`` and ``waef`` products. Search by ``forecast_datetime`` for tomorrow. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "851c6db4-79a2-43c2-a8f5-1ddc5fe80f89", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import pystac_client\n", + "import planetary_computer\n", + "from datetime import datetime, timedelta\n", + "\n", + "catalog = pystac_client.Client.open(\n", + " \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n", + " modifier=planetary_computer.sign_inplace,\n", + ")\n", + "\n", + "\n", + "forecastdt = (datetime.utcnow().replace(hour=0, minute=0, second=0, microsecond=0) + timedelta(days=1)).strftime('%Y-%m-%dT%H:%M:%SZ')\n", + "\n", + "search = catalog.search(\n", + " collections=[\"ecmwf-forecast\"],\n", + " query={\n", + " \"ecmwf:stream\": {\"in\": [\"oper\", \"wave\"]},\n", + " \"ecmwf:type\": {\"eq\": \"fc\"},\n", + " \"ecmwf:forecast_datetime\": {\"eq\": forecastdt}\n", + " },\n", + ")\n", + "items = search.item_collection()\n", + "items\n" + ] + }, + { + "cell_type": "markdown", + "id": "489ab375-fa5b-4ae7-a889-b3b873271dac", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Find the latest grib files for wave and atmospheric forecasts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6af8553b-16ac-422c-a7cc-175718f3fc4c", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "def findLatestForecast(items, stream):\n", + " stream_items = list(filter(lambda x: x.properties[\"ecmwf:stream\"] == stream, items))\n", + " #Latest forecast date\n", + " item_latest_date = max(stream_items, key=lambda item: item.properties[\"ecmwf:forecast_datetime\"]).properties[\"ecmwf:forecast_datetime\"]\n", + " #Smallest step (most recent model run)\n", + " item = min(filter(lambda x: x.properties[\"ecmwf:forecast_datetime\"] == item_latest_date, stream_items), key=lambda item: item.properties[\"ecmwf:step\"] )\n", + " return item\n", + "\n", + "oper = findLatestForecast(items, \"oper\")\n", + "wave = findLatestForecast(items, \"wave\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ff50e2cb-13a1-4736-85d0-594d5e17540c", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Download gribs from Planetary Computer\n", + "\n", + "Saving in OneLake" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09f25f89-93cc-41cb-bdf9-10e287d62dd5", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "def downloadForecast(item):\n", + " url = item.assets[\"data\"].href\n", + " id = item.id\n", + " filename = f\"/lakehouse/default/Files/{id}.grib\"\n", + " print(f\"Downloading {filename}\")\n", + " urllib.request.urlretrieve(url,filename)\n", + " urllib.request.urlcleanup()\n", + " return(filename)\n", + "\n", + "operFile = downloadForecast(oper)\n", + "waveFile = downloadForecast(wave)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6135b0a0-c8dd-41f5-8c52-e42490d6bc68", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Load and combine using xarray" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84857309-2ee1-4c81-9e90-259ecf8a3154", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "#filter to t2m (temperature - 2 metres)\n", + "ds_temps = xr.open_dataset(operFile, engine=\"cfgrib\", filter_by_keys={'typeOfLevel': 'heightAboveGround','level':2.0})\n", + "ds_waves = xr.open_dataset(waveFile, engine=\"cfgrib\")\n", + "\n", + "ds = xr.merge( [ds_temps, ds_waves])\n", + "ds\n" + ] + }, + { + "cell_type": "markdown", + "id": "b346f9c3-2ac4-4fce-8a39-d89acfd706a0", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Clip to approx Western Australia coast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a2fd568-111e-4919-af0e-9aff6c986912", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "#very approx bounding box for Western Australia\n", + "min_lat = -20\n", + "min_lon = 110\n", + "max_lat = -36\n", + "max_lon = 120\n", + "\n", + "\n", + "ds = ds.sel(latitude=slice(min_lat, max_lat), longitude=slice(min_lon,max_lon))\n", + "ds[\"t2m\"] = ds.t2m - 273\n", + "ds\n" + ] + }, + { + "cell_type": "markdown", + "id": "976cbad7-5761-499b-af57-9f033ba78f6a", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Let's plot the t2m data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9dadc14-1dab-4a76-8561-fd1d396e27f0", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10, 10))\n", + "ax = plt.axes(projection=ccrs.Mercator())\n", + "ax.coastlines(resolution=\"10m\")\n", + "plot = ds.t2m.plot(\n", + " cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "254c662d-62c7-4c38-adb6-1e19a12792a9", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "#### We can interpolate like this\n", + "\n", + "https://docs.xarray.dev/en/stable/user-guide/interpolation.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20c072de-a00d-4b81-811d-d2871de4d638", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "new_lon = np.linspace(ds.longitude[0], ds.longitude[-1], ds.dims[\"longitude\"] * 25)\n", + "new_lat = np.linspace(ds.latitude[0], ds.latitude[-1], ds.dims[\"latitude\"] * 25)\n", + "ds_i = ds.interp(latitude=new_lat, longitude=new_lon)" + ] + }, + { + "cell_type": "markdown", + "id": "1b271806-2fe0-4694-b0a4-b75d1ec47d53", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "#### Plot both OG and lerp'd data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d451471f-ca0c-4d20-91c0-ccd0e06e0aea", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "projection = ccrs.Mercator()\n", + "fig, axes = plt.subplots(ncols=2,figsize=(15,10), subplot_kw=dict(projection=projection))\n", + "axes[0].coastlines(resolution='10m')\n", + "axes[1].coastlines(resolution='10m')\n", + "\n", + "axes[0].set_title(\"Raw data\")\n", + "axes[1].set_title(\"Interpolated data\")\n", + "\n", + "ds.t2m.plot(ax=axes[0], transform=ccrs.PlateCarree());\n", + "ds_i.t2m.plot(ax=axes[1], transform=ccrs.PlateCarree());\n" + ] + }, + { + "cell_type": "markdown", + "id": "ec7496a5-9b82-491a-9b01-4f8bf5125d3d", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Extract to dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04654c76-c410-4ba7-884f-1abd263440a8", + "metadata": {}, + "outputs": [], + "source": [ + "weather_df = ds[\"t2m\"].to_dataframe().reset_index()\n", + "weather_df" + ] + }, + { + "cell_type": "markdown", + "id": "3530d669-0b47-4fff-ad22-c901a620baab", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Clean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71b4279d-70d8-4696-8adf-2d208859694a", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "weather_df.drop(['step', 'heightAboveGround','meanSea'], axis=1, inplace=True)\n", + "weather_df.rename(columns={'time': 'forecastMadeDate', 'valid_time': 'forecastForDate', 't2m': 'temperature'}, inplace=True)\n", + "weather_df.drop_duplicates()" + ] + }, + { + "cell_type": "markdown", + "id": "79a157e4-b415-4a48-9927-91e50d30001a", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Save to Lakehouse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8caa4de5-c101-4984-93c4-ab01dadd08ac", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "table_name = \"weatherModel\"\n", + "\n", + "df = spark.createDataFrame(weather_df)\n", + "df.write.mode(\"overwrite\").format(\"delta\").option(\"overwriteSchema\", \"True\").save(f\"Tables/{table_name}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "944b32d7-290b-47bb-8483-de0c4b85ecd7", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Next...\n", + "From here, we could load the ``shipwrecks`` and spatial join to the nearest forecast point, or we could map our new model forecasts into BOM maritime regions and use the existing ``shipwrecks`` to ``forecasts`` relationship, or... or...\n", + "\n", + "https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.sjoin_nearest.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "889ca475-88b7-4c8d-a4c6-9288cbd37a48", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "microsoft": { + "language": "sparksql" + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "%%sql\n", + "\n", + "SELECT * FROM weatherModel LIMIT 100" + ] + } + ], + "metadata": { + "kernel_info": { + "name": "synapse_pyspark" + }, + "kernelspec": { + "display_name": "Synapse PySpark", + "language": "Python", + "name": "synapse_pyspark" + }, + "language_info": { + "name": "python" + }, + "microsoft": { + "host": { + "synapse_widget": { + "state": {}, + "token": "9632e65b-4ed6-467c-96e4-344f20531fce" + } + }, + "language": "python", + "ms_spell_check": { + "ms_spell_check_language": "en" + } + }, + "notebook_environment": {}, + "nteract": { + "version": "nteract-front-end@1.0.0" + }, + "save_output": true, + "spark_compute": { + "compute_id": "/trident/default", + "session_options": { + "conf": {}, + "enableDebugMode": false + } + }, + "synapse_widget": { + "state": {}, + "version": "0.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/067-FabricLakehouse/Coach/Solutions/Solution - Data Engineering.ipynb b/067-FabricLakehouse/Coach/Solutions/Solution - Data Engineering.ipynb new file mode 100644 index 0000000000..5e60cad7d7 --- /dev/null +++ b/067-FabricLakehouse/Coach/Solutions/Solution - Data Engineering.ipynb @@ -0,0 +1,685 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e61ce4fd-c422-4379-b3ef-f30345cdbf79", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "# Spark Data Engineering Solution\n", + "\n", + "This is a complete solution to the data engineering aspects of the hack best performed by Python/Spark. \n", + "\n", + "**Note** XML forecast data provided by the BOM is far easier parsed and persisted by a dataflow2 than in code so not included here. \n", + "\n", + "Students can start from scratch and land data from the source, or they can start with raw, slightly cleaned (bronze), or merged (silver) data as contained in ``resources.zip``\n", + "\n", + "The final objective is to have an enriched 'Silver' zone table of shipwrecks, with their correct marine zones added.\n", + "\n", + "Be aware, that Parquet does not yet support the ``geometry`` type. Instead of mucking about with WKT, we're using geojson for our Bronze zone.\n", + "\n", + "**Exploring the Data**\n", + "\n", + "A few ideas on how students might want to explore the data along the way can be found in the notebook ``Data Exploration``. Students will probably need to at least perform a cursory glance at the data before writing to Delta, and this notebook has some pretty charts you can use to demo the exploratory process.\n" + ] + }, + { + "cell_type": "markdown", + "id": "1fac01ae-7fb8-4f95-b396-a1701b0f93f1", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Common Code\n", + "This section contains some code common to both Challenge 2 and Challenge 3." + ] + }, + { + "cell_type": "markdown", + "id": "cc64784c-211f-456b-88a4-dfc79e05b3ad", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Install Dependencies\n", + "\n", + "geopandas is needed to work with geojson files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e99f5aea-f95a-424f-92a9-443e133fe5a3", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install geopandas" + ] + }, + { + "cell_type": "markdown", + "id": "585848e7-4bb5-47c1-9098-a4d516e32507", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Setup Imports & Folder/File Paths" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae4bf5f6-b9ec-4992-b6a8-c54a80bd7c1c", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import geopandas as gp\n", + "import pyspark.sql.functions as f\n", + "\n", + "# Standard zones (gold can be considered the semantic (power bi dataset) model)\n", + "rawFilesFolder = \"/lakehouse/default/Files/Raw/\"\n", + "bronzeFilesFolder = \"/lakehouse/default/Files/Bronze/\"\n", + "silverFilesFolder = \"/lakehouse/default/Files/Silver/\"\n", + "\n", + "\n", + "# These files are created by createStudentResources.py \n", + "shipwrecksBronzeFile = f\"{bronzeFilesFolder}shipwrecks.geojson\"\n", + "marineZonesBronzeFile = f\"{bronzeFilesFolder}marinezones.geojson\"\n", + "shipwrecksSilverFile = f\"{silverFilesFolder}shipwrecks.json\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "9628f3dd-38b7-43d3-a875-82a3620262f9", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Challenge 2 - Land Ho! - Some Handy Functions to download and unzip files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f43f65e-77eb-4cda-94b5-f74ebe737a60", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "import os\n", + "import urllib\n", + "import zipfile\n", + "from re import sub\n", + "\n", + "def downloadFile(url, saveFolder):\n", + " filename = os.path.basename(url)\n", + " filepath = os.path.join(saveFolder, filename)\n", + " os.makedirs(saveFolder, exist_ok=True)\n", + " print(f\"Downloading file from URL: {url} to {filepath}\")\n", + " urllib.request.urlretrieve(url, filepath)\n", + " return(filepath)\n", + "\n", + "def unzipFile(zipfilePath, extractPath):\n", + " print(f\"Extracting {zipfilePath} to {extractPath}\")\n", + " with zipfile.ZipFile(zipfilePath, 'r') as zip_ref:\n", + " zip_ref.extractall(extractPath)\n", + " os.remove(zipfilePath)\n", + "\n", + "def toPascalCase(s):\n", + " s = sub(r\"(_|-)+\", \" \", s).title().replace(\" \", \"\").replace(\"*\",\"\")\n", + " return ''.join(s)\n" + ] + }, + { + "cell_type": "markdown", + "id": "bf77af0a-63ab-472b-b578-f6061eb259ad", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Challenge 2 Solutions\n", + "\n", + "Challenge 2 solutions are arranged per dataset - Shipwrecks, Marine Zones and Forecasts" + ] + }, + { + "cell_type": "markdown", + "id": "b6b2496f-e117-4d9f-8388-fe239dd4b138", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Shipwrecks Landing From Source - Preferred Github version\n", + "\n", + "A copy of ``Shipwrecks_WAM_002_WA_GDA94_Public.geojson`` is contained in the hack repo to allow easier access if students wish to attempt to download data themselves." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fff14ad0-bc02-4440-ac9d-e5cbe1930f0a", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "shipwrecksFileUrl = \"https://raw.githubusercontent.com/liesel-h/WhatTheHack/xxx-FabricLakehouse/067-FabricLakehouse/Student/Resources/Shipwrecks_WAM_002_WA_GDA94_Public.geojson\"\n", + "shipwrecksRawFile = downloadFile(shipwrecksFileUrl, shipwrecksRawFolder)" + ] + }, + { + "cell_type": "markdown", + "id": "2e53d5d7-bce3-4efc-9f52-abf81ceb19e7", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Shipwrecks Landing From Source - SLIP Version (Only for the advanced student)\n", + "\n", + "This is an example of importing ``Shipwrecks_WAM_002_WA_GDA94_Public_GeoJSON.zip`` from SLIP, provided for completeness. As this requires a SLIP login, a copy of this data is also provided in github, and students should probably use this mirror instead." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1fde509-17b4-4700-8563-136acfb2a022", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "def downloadSLIPFile(slipPath, slipFile, saveFolder, userId, password) :\n", + " \"\"\"\n", + " Downloads a file from West Australian government Shared Location Information Platform (SLIP)\n", + "\n", + " Code based on https://toolkit.data.wa.gov.au/hc/en-gb/articles/115000962734 \n", + " \"\"\"\n", + "\n", + " saveFile = f\"{saveFolder}/{slipFile}\"\n", + " os.makedirs(saveFile, exist_ok=True)\n", + " \n", + " dataDownloadRequestUrl = \"https://direct-download.slip.wa.gov.au/datadownload/{0}/{1}\".format(slipPath, slipFile)\n", + "\n", + "\n", + " tokenRequestUrl = \"https://sso.slip.wa.gov.au/as/token.oauth2\"\n", + " tokenRequestHeaders = { 'Authorization' : 'Basic ZGlyZWN0LWRvd25sb2Fk'}\n", + " tokenRequestForm={\"grant_type\": \"password\", \"username\":userId, \"password\":password}\n", + " tokenResponse = requests.post(tokenRequestUrl, data=tokenRequestForm, headers=tokenRequestHeaders)\n", + " accessToken=json.loads(tokenResponse.text)[\"access_token\"]\n", + "\n", + " if tokenResponse.status_code == 200:\n", + " print(f\"Downloading file from URL: {dataDownloadRequestUrl} to {saveFolder}\")\n", + " dataDownloadRequestHeaders = { 'Authorization' : 'Bearer ' + accessToken}\n", + " dataDownloadResponse = requests.get(dataDownloadRequestUrl, headers=dataDownloadRequestHeaders)\n", + " if dataDownloadResponse.status_code == 200:\n", + " with open(saveFile, 'wb') as f:\n", + " f.write(dataDownloadResponse.content)\n", + " \n", + " with zipfile.ZipFile(saveFile, 'r') as zipref:\n", + " geojsonfile=[filename for filename in zipref.namelist() if filename.endswith('.geojson')][0]\n", + " zipref.extractall(saveFolder)\n", + " return f\"{saveFolder}/{geojsonfile}\"\n", + " else:\n", + " print(\"Error download file with error \" + str(dataDownloadResponse.status_code) + \"-\" + dataDownloadResponse.text)\n", + " else:\n", + " print(\"Error getting token: \" + str(tokenResponse.status_code) + \"-\" + tokenResponse.text)\n", + "\n", + "\n", + "# SLIP username and password\n", + "# Storing creds in a notebook is never a good idea, use Key Vault\n", + "# mssparkutils.credentials.getSecret('https://SomeKeyVault.vault.azure.net/','SomeSecret')\n", + "#\n", + "# https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/microsoft-spark-utilities?pivots=programming-language-python\n", + "\n", + "SLIPUsername = \"\"\n", + "SLIPPassword = \"\"\n", + "\n", + "#The WAM-002 Shipwrecks data https://direct-download.slip.wa.gov.au/datadownload/People_and_Society/Shipwrecks_WAM_002_WA_GDA94_Public_GeoJSON.zip\n", + "SLIPFolder=\"People_and_Society\"\n", + "SLIPFile=\"Shipwrecks_WAM_002_WA_GDA94_Public_GeoJSON.zip\"\n", + "WAMsaveFolder = f\"{rawFilesFolder}WAM\"\n", + "\n", + "shipwrecksRawFile = downloadSLIPFile(SLIPFolder, SLIPFile, WAMsaveFolder, SLIPUsername, SLIPPassword)" + ] + }, + { + "cell_type": "markdown", + "id": "cf2916bd-3e69-424f-ae0c-74bd559aabba", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Shipwrecks Transforming Raw to Bronze\n", + "\n", + "Regardless of origin, the steps to transform the raw geojson to Bronze zone are the same. Here we update the CRS (see Data Exploration for why), camelCase column names, drop/rename some columns then write to our Bronze zone.\n", + "\n", + "As mentioned in the coach notes, parquet does not support ``geometry`` type, so we're saving a geojson as we'll need the geometry later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a741d28f-5784-4c93-b343-28c15f667f18", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "shipwrecksRawFile = '/lakehouse/default/Files/Raw/WAM/Shipwrecks_WAM_002_WA_GDA94_Public.geojson'\n", + "\n", + "df_shipwrecks = gp.read_file(shipwrecksRawFile)\n", + "\n", + "df_shipwrecks.to_crs('epsg:3857')\n", + "\n", + "df_shipwrecks.rename(columns=lambda x: toPascalCase(x), inplace=True)\n", + "df_shipwrecks.drop(columns={'DateDepth','TimeDepth','MaxDepth','MinDepth','BearingTo','LengthOf','ObjectId','UniqueNum'}, inplace=True)\n", + "\n", + "\n", + "df_shipwrecks.rename(columns={'TypeOfSi': 'Type', 'DateInspe': 'DateInspected', 'Long':'Lon','CountryBu':'CountryBuilt', 'Constructi': 'Construction','PortRegis':'PortRegistered', 'FileNumbe': 'FileNumber','OfficialN':'OfficialNumber','Aac': 'AAC'}, inplace=True)\n", + "df_shipwrecks.set_geometry(\"Geometry\", inplace=True)\n", + "df_shipwrecks.to_file(shipwrecksBronzeFile, driver='GeoJSON') " + ] + }, + { + "cell_type": "markdown", + "id": "04fa9460-bca5-4bce-9953-9a7d7f132a9d", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Marine Zones Landing From Source\n", + "\n", + "The BOM provides marine zone data as a zipped shapefile via FTP. Again, students can either download this from the source, or preferably start with the raw files in resources.zip." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3986d6d7-b3ad-40b3-8b2b-a13c50a4ea71", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "# WA Marine Forecast Zones IDM000003.zip\n", + "bomFtpServer = \"ftp://anonymous@ftp.bom.gov.au/\"\n", + "\n", + "coastalWatersRawFolder = f\"{rawFilesFolder}BOM\"\n", + "coastalWatersFile = f\"{bomFtpServer}anon/gen/fwo/IDW11160.xml\"\n", + "\n", + "marineZonesRawFolder = f\"{rawFilesFolder}BOM/IDM00003\"\n", + "marineZonesRawFile = f\"{marineZonesRawFolder}/IDM00003.shp\"\n", + "marineZonesBronzeFile = f\"{bronzeFilesFolder}marinezones.geojson\"\n", + "marineZonesZipFile = f\"{bomFtpServer}anon/home/adfd/spatial/IDM00003.zip\"" + ] + }, + { + "cell_type": "markdown", + "id": "a91ede7b-af77-4699-9510-d58a66012b8a", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Run to download marine zones (or use the resources.zip version instead)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7defc218-3461-42a2-a5d2-a2cbb9a90800", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "print(\"Downloading marine zones data....\")\n", + "marineZonesDownloaded = downloadFile(marineZonesZipFile, marineZonesRawFolder)\n", + "unzipFile(marineZonesDownloaded, marineZonesRawFolder)" + ] + }, + { + "cell_type": "markdown", + "id": "03bf7739-9da3-4c6a-a00a-92d5607a6151", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Marine Zones Transforming Raw to Bronze\n", + "\n", + "Similar to shipwrecks, here we updae the CRS, camelCase column names, remove/rename cols, and filter to WA coastal waters then writing to Bronze zone as geojson." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1efb3139-51ad-448a-b3b3-7135551422eb", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "df_marineZones = gp.read_file(marineZonesRawFile)\n", + "\n", + "df_marineZones.to_crs('epsg:3857')\n", + "\n", + "df_marineZones = df_marineZones[df_marineZones.STATE_CODE == \"WA\"]\n", + "df_marineZones = df_marineZones.where(df_marineZones.notna(), None)\n", + "df_marineZones.rename(columns=lambda x: toPascalCase(x), inplace=True)\n", + "df_marineZones.rename(columns={'DistName': 'DistrictName'}, inplace=True) \n", + "df_marineZones.drop(columns={'DistNo','StateCode','Type', 'Pt1Name','Pt2Name'}, inplace=True)\n", + "df_marineZones.set_geometry(\"Geometry\", inplace=True)\n", + "df_marineZones.to_file(marineZonesBronzeFile, driver='GeoJSON')" + ] + }, + { + "cell_type": "markdown", + "id": "4820f862-599c-4faf-8370-f4d7e91daf26", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Local Water Forecast Landing From Source\n", + "\n", + "The BOM provides forecast data as XML via FTP. Again, students can either download this from the source, or preferably start with the raw files in resources.zip. This file is not processed by spark, use a dataflow gen 2, it's easier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9a27dfb-5bd4-48a1-b500-4b55c2e7eb41", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "# Download the coastal waters forecast data for later use by a dataflow\n", + "# or use the version from resources.zip\n", + "# this file isn't used by this notebook\n", + "coastalWatersRawFile = downloadFile(coastalWatersFile, coastalWatersRawFolder)" + ] + }, + { + "cell_type": "markdown", + "id": "d7147d43-8abf-450a-a219-a46d39faebfc", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Challenge 3 - Enriching Shipwrecks with Marine Zone data\n", + "Using geopandas to spatial join and clean some more before writing to a delta table. For an in-depth explaination see ``Data Exploration``\n", + "\n", + "**Note** ensure the geometry column is dropped when writing to Delta!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa072467-3ebf-4906-9ede-1575ef54c365", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "spark.conf.set(\"spark.sql.execution.arrow.pyspark.enabled\", \"false\")\n", + "spark.conf.set(\"sprk.sql.parquet.vorder.enabled\", \"true\") # Enable VOrder write\n", + "spark.conf.set(\"spark.microsoft.delta.optimizeWrite.enabled\", \"true\") # Enable automatic delta optimized write\n", + "\n", + "df_marineZones = gp.read_file(marineZonesBronzeFile)\n", + "df_shipwrecks = gp.read_file(shipwrecksBronzeFile)\n", + "\n", + "df_joined = df_shipwrecks.sjoin_nearest(df_marineZones, how=\"left\", distance_col=\"distance\").query(\"distance < 5000\")\n", + "\n", + "#Drop the geometry, it's not supported by parquet\n", + "df_joined.drop(columns={'index_right', 'geometry', 'distance'}, inplace=True)\n", + "df_joined = df_joined.where(df_joined.notna(), None)\n", + "\n", + "#Save a copy in Silver (not strictly necessary)\n", + "df_joined.to_json(shipwrecksSilverFile, orient='records', lines=True)\n", + "\n", + "#Save to delta\n", + "saveTable = \"Shipwrecks\"\n", + "spark.createDataFrame(df_joined).write.mode(\"overwrite\").option(\"overwriteSchema\", \"true\").format(\"delta\").save(f\"Tables/{saveTable}\")" + ] + }, + { + "cell_type": "markdown", + "id": "295dbf23-ab03-4099-882f-bec340c81dbb", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "Alternatively, if starting from pre-enriched silver/shipwrecks.json in resources.zip \n", + "(or right click, load data...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a3a96b1-d2cd-4e63-b086-146c2e41693b", + "metadata": { + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "spark.conf.set(\"spark.sql.execution.arrow.pyspark.enabled\", \"false\")\n", + "spark.conf.set(\"sprk.sql.parquet.vorder.enabled\", \"true\") # Enable VOrder write\n", + "spark.conf.set(\"spark.microsoft.delta.optimizeWrite.enabled\", \"true\") # Enable automatic delta optimized write\n", + "\n", + "saveTable = \"Shipwrecks\"\n", + "df = spark.read.json(shipwrecksSilverFile)\n", + "df.write.mode(\"overwrite\").option(\"overwriteSchema\", \"true\").format(\"delta\").save(f\"Tables/{saveTable}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8e15aa3-9e8a-438a-9f83-c968825a90dd", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Done!\n", + "You should now have geojson raw and bronze files, and a silver zone delta table of enriched shipwrecks. \n", + "\n", + "Next, **go write a dataflow to import forecast data** (straight to silver perhaps?)\n", + "\n", + "----\n" + ] + } + ], + "metadata": { + "kernel_info": { + "name": "synapse_pyspark" + }, + "kernelspec": { + "display_name": "Synapse PySpark", + "language": "Python", + "name": "synapse_pyspark" + }, + "language_info": { + "name": "python" + }, + "microsoft": { + "host": { + "synapse_widget": { + "state": {}, + "token": "7ab78a9e-4d89-4f31-b4f2-962af741988e" + } + }, + "language": "python" + }, + "notebook_environment": {}, + "nteract": { + "version": "nteract-front-end@1.0.0" + }, + "save_output": true, + "spark_compute": { + "compute_id": "/trident/default", + "session_options": { + "conf": {}, + "enableDebugMode": false + } + }, + "synapse_widget": { + "state": {}, + "version": "0.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/067-FabricLakehouse/Coach/Solutions/createStudentResources.py b/067-FabricLakehouse/Coach/Solutions/createStudentResources.py new file mode 100644 index 0000000000..da3947525c --- /dev/null +++ b/067-FabricLakehouse/Coach/Solutions/createStudentResources.py @@ -0,0 +1,112 @@ +import os +import urllib +import zipfile +from re import sub + +import geopandas as gp + + +def downloadFile(url, saveFolder): + filename = os.path.basename(url) + filepath = os.path.join(saveFolder, filename) + os.makedirs(saveFolder, exist_ok=True) + print(f"Downloading file from URL: {url} to {filepath}") + urllib.request.urlretrieve(url, filepath) + return(filepath) + +def unzipFile(zipfilePath, extractPath): + print(f"Extracting {zipfilePath} to {extractPath}") + with zipfile.ZipFile(zipfilePath, 'r') as zip_ref: + zip_ref.extractall(extractPath) + os.remove(zipfilePath) + + +def zipdir(path, ziph, base_path): + for root, dirs, files in os.walk(path): + for file in files: + file_path = os.path.join(root, file) + relative_file_path = os.path.relpath(file_path, base_path) + if os.path.basename(file_path) != os.path.basename(ziph.filename): + ziph.write(file_path, relative_file_path) + +def toPascalCase(s): + s = sub(r"(_|-)+", " ", s).title().replace(" ", "").replace("*","") + return ''.join(s) + + +outputFolder = "data/" +resourcesZipFilename = f"{outputFolder}resources.zip" + +# Zones +rawFilesFolder = f"{outputFolder}Raw/" +bronzeFilesFolder = f"{outputFolder}Bronze/" +silverFilesFolder = f"{outputFolder}Silver/" + +os.makedirs(rawFilesFolder, exist_ok=True) +os.makedirs(bronzeFilesFolder, exist_ok=True) +os.makedirs(silverFilesFolder, exist_ok=True) + +# WA coastal waters forecast IDW11160.xml amd Marine Forecast Zones IDM000003.zip +bomFtpServer = "ftp://anonymous@ftp.bom.gov.au/" + +coastalWatersRawFolder = f"{rawFilesFolder}BOM" +coastalWatersFile = f"{bomFtpServer}anon/gen/fwo/IDW11160.xml" + +marineZonesRawFolder = f"{rawFilesFolder}BOM/IDM00003" +marineZonesRawFile = f"{marineZonesRawFolder}/IDM00003.shp" +marineZonesBronzeFile = f"{bronzeFilesFolder}marinezones.geojson" +marineZonesZipFile = f"{bomFtpServer}anon/home/adfd/spatial/IDM00003.zip" + +#Shipwrecks (from repo) +shipwrecksRawFolder = f"{rawFilesFolder}WAM" +shipwrecksBronzeFile = f"{bronzeFilesFolder}shipwrecks.geojson" +shipwrecksSilverFile = f"{silverFilesFolder}shipwrecks.json" +shipwrecksFile = "https://raw.githubusercontent.com/liesel-h/WhatTheHack/xxx-FabricLakehouse/067-FabricLakehouse/Coach/Solutions/data/Shipwrecks_WAM_002_WA_GDA94_Public.geojson" + + +#Download files to Raw +print("Downloading data....") +shipwrecksRawFile = downloadFile(shipwrecksFile, shipwrecksRawFolder) +coastalWatersRawFile = downloadFile(coastalWatersFile, coastalWatersRawFolder) +marineZonesDownloaded = downloadFile(marineZonesZipFile, marineZonesRawFolder) +unzipFile(marineZonesDownloaded, marineZonesRawFolder) + + + +print("Processing raw files...") +#Read Files +df_shipwrecks = gp.read_file(shipwrecksRawFile) +df_marineZones = gp.read_file(marineZonesRawFile).to_crs(df_shipwrecks.crs) #Normalise CRS of both spatial datasets + + +#Clean Files, save to Bronze zone +print("Shipwrecks...") +df_shipwrecks.rename(columns=lambda x: toPascalCase(x), inplace=True) +df_shipwrecks.drop(columns={'DateDepth','TimeDepth','MaxDepth','MinDepth','BearingTo','LengthOf','ObjectId','UniqueNum'}, inplace=True) + + +df_shipwrecks.rename(columns={'TypeOfSi': 'Type', 'DateInspe': 'DateInspected', 'Long':'Lon','CountryBu':'CountryBuilt', 'Constructi': 'Construction','PortRegis':'PortRegisitered', 'FileNumbe': 'FileNumber','OfficialN':'OfficialNumber','Aac': 'AAC'}, inplace=True) +df_shipwrecks.set_geometry("Geometry", inplace=True) +df_shipwrecks.to_file(shipwrecksBronzeFile, driver='GeoJSON') + +print("Marine Zones...") +df_marineZones = df_marineZones[df_marineZones.STATE_CODE == "WA"] +df_marineZones = df_marineZones.where(df_marineZones.notna(), None) +df_marineZones.rename(columns=lambda x: toPascalCase(x), inplace=True) +df_marineZones.rename(columns={'DistName': 'DistrictName'}, inplace=True) +df_marineZones.drop(columns={'DistNo','StateCode','Type', 'Pt1Name','Pt2Name'}, inplace=True) +df_marineZones.set_geometry("Geometry", inplace=True) +df_marineZones.to_file(marineZonesBronzeFile, driver='GeoJSON') + +print("Joining datasets...") +#Spatial Join shipwrecks and marine zones +df_joined = df_shipwrecks.sjoin_nearest(df_marineZones, how="left", distance_col="distance").query("distance < 5000") +#Drop the geometry, it's not supported by parquet +df_joined.drop(columns={'index_right', 'geometry', 'distance'}, inplace=True) +df_joined = df_joined.where(df_joined.notna(), None) +df_joined.to_json(shipwrecksSilverFile, orient='records', lines=True) + +print(f"Zipping {resourcesZipFilename}") +resourcesZip = zipfile.ZipFile(resourcesZipFilename, 'w', zipfile.ZIP_DEFLATED) +zipdir(outputFolder, resourcesZip, outputFolder) +resourcesZip.close() \ No newline at end of file diff --git a/067-FabricLakehouse/Coach/Solutions/data/Shipwrecks_WAM_002_WA_GDA94_Public.geojson b/067-FabricLakehouse/Coach/Solutions/data/Shipwrecks_WAM_002_WA_GDA94_Public.geojson new file mode 100644 index 0000000000..a0cd4d7f9a --- /dev/null +++ b/067-FabricLakehouse/Coach/Solutions/data/Shipwrecks_WAM_002_WA_GDA94_Public.geojson @@ -0,0 +1,17391 @@ +{ + "type" : "FeatureCollection", + "name" : "Shipwrecks_WAM_002", + "features" : [ + { + "type" : "Feature", + "geometry" : { + "type" : "Point", + "coordinates" : [ 122.245600001, -17.972799999 ] + }, + "properties" : { + "ogc_fid" : 1, + "name" : "Broome Flying Boats", + "type_of_si" : "Aircraft", + "rig" : "", + "when_lost" : "1942/03/03", + "protected" : "Protected Heritage WA Act", + "date_inspe" : "", + "where_lost" : "Broome, Roebuck Bay", + "region" : "North West (Broome Area)", + "lat" : -17.9728, + "long" : 122.2456, + "position_i" : "", + "constructi" : "", + "engine" : "N", + "tona" : "", + "tonb" : "", + "country_bu" : "Unknown", + "port_built" : "", + "when_built" : "", + "port_regis" : "", + "official_n" : "", + 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Located during Barrow Island Archaeological Project (BIAP).", + "industry_1" : "Fisheries", + "industry_2" : "pearling", + "url" : "", + "length_of_" : "", + "bearing_to" : "", + "min_depth" : 0, + "date_depth" : "", + "time_depth" : "", + "max_depth" : 0, + "unique_num" : 1681, + "object_id" : 300 + } + }, + { + "type" : "Feature", + "geometry" : { + "type" : "Point", + "coordinates" : [ 122.213830001, -17.995469999 ] + }, + "properties" : { + "ogc_fid" : 298, + "name" : "Unidentified refugee boat", + "type_of_si" : "Shipwreck", + "rig" : "", + "when_lost" : "", + "protected" : "Not protected Federal", + "date_inspe" : "", + "where_lost" : "Roebuck Bay", + "region" : "North West (Broome Area)", + "lat" : -17.99547, + "long" : 122.21383, + "position_i" : "GPS2010", + "constructi" : "Wooden", + "engine" : "", + "tona" : "", + "tonb" : "", + "country_bu" : "", + "port_built" : "", + "when_built" : "", + "port_regis" : "", + "official_n" : "", + "registrati" : "", + "length" : "", + 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"Wooden", + "engine" : "Gardiner 3LW", + "tona" : "", + "tonb" : "", + "country_bu" : "Australia", + "port_built" : "", + "when_built" : "", + "port_regis" : "", + "official_n" : "", + "registrati" : "", + "length" : "", + "beam" : "", + "draft" : "", + "passengers" : "", + "deaths" : "", + "crew" : "", + "sinking" : "", + "sunk_code" : "", + "port_from" : "", + "port_to" : "", + "master" : "", + "owner" : "", + "file_numbe" : "", + "when_found" : "", + "sources" : "Wreck report No. 188", + "industry_1" : "Fisheries", + "industry_2" : "", + "url" : "", + "length_of_" : "", + "bearing_to" : "", + "min_depth" : 0, + "date_depth" : "", + "time_depth" : "", + "max_depth" : 0, + "unique_num" : 1685, + "object_id" : 302 + } + }, + { + "type" : "Feature", + "geometry" : { + "type" : "Point", + "coordinates" : [ 115.131133339, -21.648333328 ] + }, + "properties" : { + "ogc_fid" : 300, + "name" : "Beadon Creek UNID", + "type_of_si" : "Shipwreck", + "rig" : "Unknown", + "when_lost" : "", + "protected" : "Unknown", + "date_inspe" : "", + "where_lost" : "Beadon Creek, Onslow", + "region" : "North West (Onslow Area)", + "lat" : -21.64833333, + "long" : 115.13113333, + "position_i" : "GPS", + "constructi" : "Wooden", + "engine" : "", + "tona" : "", + "tonb" : "", + "country_bu" : "", + "port_built" : "", + "when_built" : "", + "port_regis" : "", + "official_n" : "", + "registrati" : "", + "length" : "", + "beam" : "", + "draft" : "", + "passengers" : "", + "deaths" : "", + "crew" : "", + "sinking" : "", + "sunk_code" : "", + "port_from" : "", + "port_to" : "", + "master" : "", + "owner" : "", + "file_numbe" : "", + "when_found" : "", + "sources" : "Wreck Report 192WINC Report", + "industry_1" : "", + "industry_2" : "", + "url" : "", + "length_of_" : "", + "bearing_to" : "", + "min_depth" : 0, + "date_depth" : "", + "time_depth" : "", + "max_depth" : 0, + "unique_num" : 1687, + "object_id" : 303 + } + }, + { + "type" : "Feature", + "geometry" : { + "type" : "Point", + "coordinates" : [ 115.649433344, -33.322066658 ] + }, + "properties" : { + "ogc_fid" : 301, + "name" : "Bunbury Excavation East Lot 882 Trench 1", + "type_of_si" : "Shipwreck", + "rig" : "Unknown", + "when_lost" : "", + "protected" : "Protected State", + "date_inspe" : "24/11/2011", + "where_lost" : "Koombana Bay, Bunbury", + "region" : "SW Coast (Bunbury)", + "lat" : -33.32206667, + "long" : 115.64943333, + "position_i" : "", + "constructi" : "Wooden", + "engine" : "", + "tona" : "", + "tonb" : "", + "country_bu" : "", + "port_built" : "", + "when_built" : "", + "port_regis" : "", + "official_n" : "", + "registrati" : "", + "length" : "", + "beam" : "", + "draft" : "", + "passengers" : "", + "deaths" : "", + "crew" : "", + "sinking" : "", + "sunk_code" : "", + "port_from" : "", + "port_to" : "", + "master" : "", + "owner" : "", + "file_numbe" : "", + "when_found" : "", + "sources" : "Anderson & McAllister, 2012, Koombana Bay foreshore maritime archaeological survey and excavations 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"", + "draft" : "", + "passengers" : "", + "deaths" : "", + "crew" : "", + "sinking" : "", + "sunk_code" : "", + "port_from" : "", + "port_to" : "", + "master" : "", + "owner" : "", + "file_numbe" : "", + "when_found" : "", + "sources" : "", + "industry_1" : "", + "industry_2" : "", + "url" : "", + "length_of_" : "", + "bearing_to" : "", + "min_depth" : 0, + "date_depth" : "", + "time_depth" : "", + "max_depth" : 0, + "unique_num" : 1695, + "object_id" : 305 + } + } + ] +} \ No newline at end of file diff --git a/067-FabricLakehouse/Coach/Solutions/data/license.txt b/067-FabricLakehouse/Coach/Solutions/data/license.txt new file mode 100644 index 0000000000..a3f8dcd698 --- /dev/null +++ b/067-FabricLakehouse/Coach/Solutions/data/license.txt @@ -0,0 +1 @@ +Shipwrecks (WAM-002) https://catalogue.data.wa.gov.au/dataset/shipwrecks by Western Australian Museum, CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ \ No newline at end of file diff --git a/067-FabricLakehouse/Coach/Solutions/data/metadata.txt b/067-FabricLakehouse/Coach/Solutions/data/metadata.txt new file mode 100644 index 0000000000..e49baee697 --- /dev/null +++ b/067-FabricLakehouse/Coach/Solutions/data/metadata.txt @@ -0,0 +1,6 @@ +1. Custodian provided geometry in GDA94 +2. Upload + a. Transformed Geometry from GDA94 to WGS84 +3. Download + a. Transformed Geometry from WGS84 to GDA94 + diff --git a/067-FabricLakehouse/Coach/Solutions/troubleshooting/Cancel-Dataflow.ps1 b/067-FabricLakehouse/Coach/Solutions/troubleshooting/Cancel-Dataflow.ps1 new file mode 100644 index 0000000000..7e9fce6eb8 --- /dev/null +++ b/067-FabricLakehouse/Coach/Solutions/troubleshooting/Cancel-Dataflow.ps1 @@ -0,0 +1,29 @@ +Connect-PowerBIServiceAccount + +$group = "19742052-c60e-4f83-8ec7-d7a060045293" +$dataflows = Invoke-PowerBIRestMethod -Url "https://api.powerbi.com/v1.0/myorg/groups/${group}/dataflows" -Method GET + +#9f1961e7-f159-40c4-a6d9-b872a7df54af/transactions/ -Method GET +#Invoke-PowerBIRestMethod -Url https://api.powerbi.com/v1.0/myorg/groups/19742052-c60e-4f83-8ec7-d7a060045293/dataflows/transactions/a184d57d-628a-4d07-bc1b-21dc2a884084$5386940/cancel -Method POST +#POST https://api.powerbi.com/v1.0/myorg/groups/{groupId}/dataflows/transactions/{transactionId}/cancel + + +$dfs = ConvertFrom-Json $dataflows +foreach ($dataflow in $dfs.value) { + $dataflowId = $dataflow.objectId + $txs = Invoke-PowerBIRestMethod -Url "https://api.powerbi.com/v1.0/myorg/groups/${group}/dataflows/${dataflowId}/transactions" -Method GET + $txs = ConvertFrom-Json $txs + + + foreach ($tx in $txs.value) { + if($tx.status -eq "InProgress") { + $id = $tx.id + Invoke-PowerBIRestMethod -Url "https://api.powerbi.com/v1.0/myorg/groups/${group}/dataflows/transactions/${id}/cancel" -Method POST + } + } +} + +foreach ($dataflow in $dfs.value) { + $dataflowId = $dataflow.objectId + Invoke-PowerBIRestMethod -Url "https://api.powerbi.com/v1.0/myorg/groups/${group}/dataflows/${dataflowId}" -Method DELETE +} \ No newline at end of file diff --git a/067-FabricLakehouse/Coach/image.png b/067-FabricLakehouse/Coach/image.png new file mode 100644 index 0000000000..aef1e93452 Binary files /dev/null and b/067-FabricLakehouse/Coach/image.png differ diff --git a/067-FabricLakehouse/Coach/images/dataflowcreds.png b/067-FabricLakehouse/Coach/images/dataflowcreds.png new file mode 100644 index 0000000000..973d568843 Binary files /dev/null and b/067-FabricLakehouse/Coach/images/dataflowcreds.png differ diff --git a/067-FabricLakehouse/Coach/images/dataflowgen2.png b/067-FabricLakehouse/Coach/images/dataflowgen2.png new file mode 100644 index 0000000000..42f53340fd Binary files /dev/null and b/067-FabricLakehouse/Coach/images/dataflowgen2.png differ diff --git a/067-FabricLakehouse/Coach/images/destination.png b/067-FabricLakehouse/Coach/images/destination.png new file mode 100644 index 0000000000..91b1915811 Binary files /dev/null and b/067-FabricLakehouse/Coach/images/destination.png differ diff --git a/067-FabricLakehouse/Coach/images/forecasts.png b/067-FabricLakehouse/Coach/images/forecasts.png new file mode 100644 index 0000000000..252e0cd74b Binary files /dev/null and b/067-FabricLakehouse/Coach/images/forecasts.png differ diff --git a/067-FabricLakehouse/Coach/images/importdataflow.png b/067-FabricLakehouse/Coach/images/importdataflow.png new file mode 100644 index 0000000000..f729568d46 Binary files /dev/null and b/067-FabricLakehouse/Coach/images/importdataflow.png differ diff --git a/067-FabricLakehouse/Coach/images/modelview.png b/067-FabricLakehouse/Coach/images/modelview.png new file mode 100644 index 0000000000..6a32d1e501 Binary files /dev/null and b/067-FabricLakehouse/Coach/images/modelview.png differ diff --git a/067-FabricLakehouse/Coach/images/newdataflow.png b/067-FabricLakehouse/Coach/images/newdataflow.png new file mode 100644 index 0000000000..75e0d62e28 Binary files /dev/null and b/067-FabricLakehouse/Coach/images/newdataflow.png differ diff --git a/067-FabricLakehouse/Coach/images/semanticmodel.png b/067-FabricLakehouse/Coach/images/semanticmodel.png new file mode 100644 index 0000000000..ce968a7f4a Binary files /dev/null and b/067-FabricLakehouse/Coach/images/semanticmodel.png differ diff --git a/067-FabricLakehouse/Coach/images/sqlanalysisendpoint.png b/067-FabricLakehouse/Coach/images/sqlanalysisendpoint.png new file mode 100644 index 0000000000..2691d81e2d Binary files /dev/null and b/067-FabricLakehouse/Coach/images/sqlanalysisendpoint.png differ diff --git a/067-FabricLakehouse/Coach/images/uploadfolder.png b/067-FabricLakehouse/Coach/images/uploadfolder.png new file mode 100644 index 0000000000..67f25f0ba7 Binary files /dev/null and b/067-FabricLakehouse/Coach/images/uploadfolder.png differ diff --git a/067-FabricLakehouse/Coach/images/workspacelakehouseid.png b/067-FabricLakehouse/Coach/images/workspacelakehouseid.png new file mode 100644 index 0000000000..e738bcd354 Binary files /dev/null and b/067-FabricLakehouse/Coach/images/workspacelakehouseid.png differ diff --git a/067-FabricLakehouse/README.md b/067-FabricLakehouse/README.md new file mode 100644 index 0000000000..fb7b7cec5b --- /dev/null +++ b/067-FabricLakehouse/README.md @@ -0,0 +1,47 @@ +# What The Hack - Dive in Microsoft Fabric Lakehouse + +## Introduction + +![](Student/images/WTH.png) + + Washed by the warm [Leeuwin Current](https://en.wikipedia.org/wiki/Leeuwin_Current), the coastal waters of Western Australia are a divers paradise, home to a diverse range of marine life including the world's largest fish, the [Whale Shark](https://en.wikipedia.org/wiki/Whale_shark). + + On a more sombre note, these waters have also claimed more than [1400 shipwrecks](https://en.wikipedia.org/wiki/Shipwrecks_of_Western_Australia). Some of these wrecks are well known, such as the [Batavia](https://en.wikipedia.org/wiki/Batavia_(ship)) and the [HMS Sydney](https://en.wikipedia.org/wiki/HMAS_Sydney_(D48)), and are protected archaeological sites. But many are regularly explored by intrepid divers and snorkelers, having become popular tourist attractions. + +Margie's Travel is a travel agency specialising in diving holidays. They have heard about Microsoft Fabric, and have asked you to help them build a solution to allow their customers to explore the possibilities of diving some of the shipwrecks of Western Australia. + + Your task is to use Microsoft Fabric to bring together the data and stories behind these shipwrecks to help boost customer engagement. Margie's would also like to be able to use this data to help customers assess dive conditions a few days before, and as a basis for on-board dive briefings. + + +## Learning Objectives + +In this hack you will + +1. Provision a Fabric Lakehouse and "dive" on in. +2. "Descend" in to the depths of open data sources including spatial, weather and archaeological to help build a data story behind the shipwrecks of Western Australia. +3. "Land" your data in your Fabric Lakehouse and "surface" with a clean, combined dataset ready for analysis. +4. Make a "splash" and bring your findings to life. + +## Challenges + +- Challenge 00: **[Prerequisites - Grab your fins and a full tank!](Student/Challenge-00.md)** + - Provision your Fabric Lakehouse +- Challenge 01: **[Finding Data](Student/Challenge-01.md)** + - Head out into open waters to find your data +- Challenge 02: **[Land ho!](Student/Challenge-02.md)** + - Land your data in your Fabric Lakehouse +- Challenge 03: **[Swab the Decks!](Student/Challenge-03.md)** + - Clean and combine your datasets ready for analysis +- Challenge 04: **[Make a Splash](Student/Challenge-04.md)** + - Build a data story to bring your findings to life +- Challenge 05: **[Giant Stride](Student/Challenge-05.md)** + - Take a giant stride and share your data story with the world + +## Prerequisites + +- A Microsoft Fabric subscription & potentially, a Power BI Pro license (see **[Challenge 00](Student/Challenge-00.md)**) +- Some familiarity with Fabric and Power BI (or a willingness to learn!) + +## Contributors + +- Liesel Hughes, Cloud Solution Architect, Microsoft, Boorloo - Whadjuk country (Perth, Western Australia) diff --git a/067-FabricLakehouse/Student/Challenge-00.md b/067-FabricLakehouse/Student/Challenge-00.md new file mode 100644 index 0000000000..64a62d150b --- /dev/null +++ b/067-FabricLakehouse/Student/Challenge-00.md @@ -0,0 +1,81 @@ +# Challenge 0 - Prerequisites - Grab your fins and a full tank! + +**[Home](../README.md)** - [Next Challenge >](./Challenge-01.md) + +## Introduction + +Thank you for participating in the Fabric Lakehouse What The Hack. Before you can hack, you will need to set up some prerequisites. + +## Prerequisites + +You will need: + +- A Microsoft Fabric subscription. At the time of writing, Microsoft Fabric is in public preview and a free trial is available at [aka.ms/FabricTrial](https://aka.ms/FabricTrial). + +- A Microsoft Fabric enabled workspace. Either you or your organisation's administrator will need to create this. Instructions can be found at [learn.microsoft.com/en-us/fabric/get-started/create-workspaces](https://learn.microsoft.com/en-us/fabric/get-started/create-workspaces) + +- Power BI desktop. This can be installed from [aka.ms/getpowerbi](https://aka.ms/getpowerbi) + +- A love of terrible sea-themed puns + +## Setting up your environment + +You will need to complete the following steps to set up your environment: + +1. Create a new workspace in Fabric (or have one created by your administrator). +2. Create a Lakehouse in your workspace. + + +### Create a new workspace in Fabric + +If you are not familiar with Fabric workspaces, read [Create a workspace](https://learn.microsoft.com/en-us/fabric/get-started/create-workspaces) on Learn. When creating your workspace, you may need to set the workspace's Fabric license. + +Expand Advanced Settings and select the appropriate license. If you are using a trial subscription, select the Trial license, or select the Fabric capacity you have been assigned by your administrator. + +![](images/workspace-advanced-settings.png) + +### Create a Lakehouse in your workspace + +Create a new Lakehouse in your workspace by following the instructions at [Create a Lakehouse](https://learn.microsoft.com/en-us/fabric/data-engineering/create-lakehouse). + +## Success Criteria + +1. You have logged into [fabric.microsoft.com](https://fabric.microsoft.com) +1. You have Power BI installed on your PC. +1. You have created a new workspace, a Lakehouse and uploaded the provided data files to your OneLake. + +You're now ready to grab a tank, your fins and set sail! + +---- + +### About that resources.zip file + + +You might be wondering what that ``resources.zip`` file your Coach provided you is all about? Divers always use the buddy system, and we've buddied up with you in case you hit rough water when finding Nemo during your hack. + +
+ But for now, spoilers! No peeking! + +## Hey, we said no peeking! + +### But Help! I'm lost at sea! + +Your Coach has provided some data to help you out during this hack. Of course, you are free to source your own data, but if you end up in shark infested waters you can always fall back on the data provided by your Coach. + +If you need it, you can upload the data (or parts of it) to your Lakehouse by following the instructions below. + +#### Uploading the Data + +Unzip the ``resources.zip`` file provided by your coach. In your new Lakehouse, click on the ``...`` menu next to the ``Files`` item and select ``Upload -> Upload Folder`` + +![](images/upload-folder.png) + +Navigate to the folder containing the unzipped resources and select the folder to upload (in this case Raw). Click Upload. + +![](images/upload-raw.png) + +Repeat for any of the other folders. + +![](images/upload-all.png) + +
diff --git a/067-FabricLakehouse/Student/Challenge-01.md b/067-FabricLakehouse/Student/Challenge-01.md new file mode 100644 index 0000000000..125fffbb7d --- /dev/null +++ b/067-FabricLakehouse/Student/Challenge-01.md @@ -0,0 +1,57 @@ +# Challenge 1 - Finding Data + +[< Previous Challenge](./Challenge-00.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-02.md) + +## Pre-requisites + +Read [the Brief!](../README.md) + +## Introduction + +The brief from Margie's Travel is fairly open-ended, so the first step is to consider what data sources might be useful to help build a story around the shipwrecks of Western Australia. Don't forget the second requirement for assessing conditions and pre-dive briefings! + +## Description + +In this challenge you will first brainstorm categories of data you'd like to include. You'll then need to explore some of the open data sources available to you, and consider how they might be used to help build your story. + +Next, you'll select a few data sources to use in your solution and document your choices. Don't forget licensing considerations! Remember to think about open data sources, any potential downsides of using your data provider (like availability, cost, etc). + +Finally, you'll consider each of your sources and document which technology in Fabric would be most appropriate to retrieve, clean and store the data. You'll also need to consider how you will present your data stories and dive briefings. + +Some hints to get you started: + +- What makes a compelling story and how could this be presented? +- What kinds of data might be useful to help build a story around the shipwrecks of Western Australia? +- What would you want to know before heading out in a dive boat this weekend? +- Which organisations might have data that could be useful? +- When would you use a Notebook, a Dataflow or some other mechanism? + +Now, it's time to hit up your favourite search or chat app and cast your nets for that data! + +## Success Criteria + +To complete this challenge successfully, you should: + +- Identify 2-3 data sources that you will use in your solution, including a brief description, how the data can be retrieved and the licensing model of each. No need to code anything just yet! +- Identify the method you plan to use to retrieve, clean and store each of your data sources, as well as the method you will use to present your data stories and dive briefings +- Document your choices (a simple table or bullet points is fine). You'll need this later on! + +## Learning Resources + +### Fabric Documentation + +- [Getting Started with Fabric](https://learn.microsoft.com/en-us/fabric/get-started/) +- [Microsoft Fabric Pipeline Dataflow Spark Decision Guide](https://learn.microsoft.com/en-us/fabric/get-started/decision-guide-pipeline-dataflow-spark) + +### Australian Government Links + +- [WA Museum's](https://museum.wa.gov.au/museums/) list of museums in Western Australia - they might have an interest in under water archaeology and maybe even some data? +- [The Australian Bureau of Meteorology](http://www.bom.gov.au/) provides a range of data services +- [Data.gov.au](https://data.gov.au/data/about) is the central source of Australian open government data + +## Tips & Hints + +- The WA coast can be a dangerous place. Be sure to **check the swell** before heading out! +- Spatial data doesn't just cover the land. But shapefiles can be tricky to work with without the right tools. Maybe ask a geo-savvy [friend for help?](https://en.wikipedia.org/wiki/Giant_panda) (Too obscure? Hint: ``geopandas``) +- Don't be put off by a registration page, many organisations are happy to share their data, but they like to know who with. But if you don't want to share your details, there's an *ocean* of other sources to choose from. +- Seas too rough? Lost at sea? Really run aground with not a parrot or a cracker to help? Your friendly coach has already been out on the boat this morning and has a few ideas to get you started. If you're really really stuck, what hidden treasure lurks in your Lake already? diff --git a/067-FabricLakehouse/Student/Challenge-02.md b/067-FabricLakehouse/Student/Challenge-02.md new file mode 100644 index 0000000000..ca0797842c --- /dev/null +++ b/067-FabricLakehouse/Student/Challenge-02.md @@ -0,0 +1,79 @@ +# Challenge 02 - Land ho! + +[< Previous Challenge](./Challenge-01.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-03.md) + +## Introduction + +Now you've found and documented your data sources, it's time to land your data in OneLake and to start to process it into something more valuable. + +## Description + +In this challenge you will develop a solution to: + +- Land your raw data in OneLake. +- Process this data. You will need to consider how you will process the data (notebook, dataflow etc) and where you will store it in OneLake. + +First, start by reading the background on Lakehouse design below. + +Next, you will need to download your source data and land it in OneLake. For this step, you're free to write code, a pipeline and/or dataflow to retrieve your data, or, if you prefer, you can manually download the data and upload it to OneLake. + +Finally, you will need to process your data - gently. At this stage you should only be doing some basic processing, eg. data type conversion and possibly loaded into a table (or stored in some other suitable format) but very similar in appearance to the source data. + +If you get really stuck, don't worry! Your Coach is here to help and has provided you with a little something to un-stick you. Remember we said no peeking in [Challenge 0](./Challenge-00.md)? If you're really stuck, talk to your coach and go have a peek at the notes at the end of [Challenge 0](./Challenge-00.md). + +## Success Criteria + +To complete this challenge successfully, you should: + +- Have provisioned any Fabric resources required to land your data +- Demonstrate how you have retrieved each dataset (automatically, manually or a combination of both) +- Show that your data is stored as-is in OneLake following a naming convention/location of your choice +- Show that you have processed your data (eg. data type conversion, renaming columns, removing rows with errors, etc) and stored it in OneLake following your Lakehouse naming convention + +Error handling is always good, but not required for this challenge + +## Background - Lakehouse Design + +This section provides some background on the design of the Lakehouse for this challenge. + +A [Fabric Lakehouse] (https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-overview) can be defined as a data architecture platform for storing, managing, and analyzing structured and unstructured data in a single location. Fabric leverages [Delta Lake](https://delta.io/) to store data in a lakehouse and expose as tables, similar to a more traditional database. It's these tables we can use query with SQL, Python or other tools like Excel and Power BI. Fabric can also store plain files (text, docx, png etc) in the Lakehouse, useful for use cases like data science and machine learning. + +One of the key decisions when building a data lake / Lakehouse is _how_ to arrange your data - we're wanting to go diving in the ocean, not squelching in the [swamp](https://en.wikipedia.org/wiki/Data_lake#Criticism). + +A standard way of organising your Lakehouse is to use a "pattern" that divides your data in to different zones: + +- Raw - The data landed as-is +- Bronze - Some basic processing, eg. data type conversion and possibly loaded into a table (or stored in some other suitable format) but very similar in appearance to the raw data. (Sometimes raw and bronze are amalgamated into a single zone) +- Silver - Pieces of Eight! Now we're talking. The data is cleaned, transformed and ready for us to use. Silver zones are nearly always tables (for structured data) and the data is often aggregated and/or enriched with other data sources. +- Gold - Ah, Doubloons! Even more valuable, this is large scale data sets that have been aggregated, modelled and locked up in a chest for safe keeping. X marks the spot! In the Fabric world, this is often a Semantic model (aka Power BI dataset), with measures and relationships built-in + +(Sometimes you might also see a "Platinum" zone for data that has been further enriched with external data sources, but we'll leave that for another day.) + +Typically these would be top level folders, but may also be more of a logical grouping, for example by schema or naming convention. + +**In this challenge**, it sounds like you should be landing our data in the "Raw" Files zone and cleaning it to a "Bronze" zone ;) ready to turn into something more precious in [the next Challenge](./Challenge-03.md). + +For more info on Lakehouses, check out the Learning Resources below. + +## Learning Resources + +- What is [OneLake](https://learn.microsoft.com/en-us/fabric/onelake/onelake-overview)? +- [Fabric Notes](https://aka.ms/FabricNotes) +- [What is the medallion lakehouse architecture?](https://learn.microsoft.com/en-us/azure/databricks/lakehouse/medallion) + +- Fabric has dozens of connectors eg. [HTTP](https://learn.microsoft.com/en-us/fabric/data-factory/connector-http), [REST](https://learn.microsoft.com/en-us/fabric/data-factory/connector-rest-overview) and [OData](https://learn.microsoft.com/en-us/fabric/data-factory/connector-odata-overview) +- Python's [``urllib``](https://docs.python.org/3/library/urllib.html) supports a number of protocols including HTTP and FTP + +## Tips + +- Data providers are often very helpful and may be able to provide you with a code sample if you hunt around their web sites. +- Landing the data automatically with code is great, but you may want to start by downloading the data using a browser first. If coding automated retrieval really doesn't float your boat, don't fall overboard, it's fine to manually download the data and then upload it to OneLake. + +## Advanced Challenges (Optional) + +Too comfortable? Eager to do more? Try these additional challenges! + +- Schedule your data retrieval +- Archive your raw data before downloading the latest version +- Add some notification logic to alert you on what's going on with your process +- Error handling! What happens if your data source is unavailable? What happens if your data source changes format? What happens if your data source is updated while you're processing it? diff --git a/067-FabricLakehouse/Student/Challenge-03.md b/067-FabricLakehouse/Student/Challenge-03.md new file mode 100644 index 0000000000..3d9d1d57a6 --- /dev/null +++ b/067-FabricLakehouse/Student/Challenge-03.md @@ -0,0 +1,44 @@ +# Challenge 03 - Swab the Decks! + +[< Previous Challenge](./Challenge-02.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-04.md) + +## Introduction + +You've dived into an ocean of open datasets, found your data and have raw files sitting in OneLake. You've also started to polish it up into shiny bronze. + +Now it's time to turn that bronze into silver - clean and combine your datasets ready for analysis. + +Get Your Data Ship Shape! + +## Description + +In this challenge, you will develop a solution to prep your datasets ready for analysis. (In Lakehouse speak, this is the silver zones). + +Remember the requirements from Margie's Travel? You may find gaps in your data, perhaps you need another dataset in order to blend and enrich your data. You may also need to clean your data, keeping some features and discarding others. + +You'll want to check out your data along the way, so make use of Fabric's awesome SQL Endpoint to run some ad hoc queries on your silver data so far. You can also use Power BI to make an 'exploratory report' to help you understand your data. Spark notebooks make great exploratory tools too, if coding is your thing. + +So, grab your mop and bucket and get cleaning up your data! + +## Success Criteria + +To complete this challenge successfully, you should be able to: + +- Show how you have cleaned and combined your datasets, and explain why you chose a particular method (e.g., a notebook, or a copy data activity) +- Show that you have written your data to one (or more) delta tables in your Lakehouse +- Demonstrate that your data is now ready to build your data story (This could be an exploratory dashboard, or a notebook, or even just some sample queries) + +## Learning Resources + +- [Use a notebook to load data into your Lakehouse](https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-notebook-load-data) +- [Create your first dataflow to get and transform data](https://learn.microsoft.com/en-us/fabric/data-factory/create-first-dataflow-gen2) +- Power Query (as used in Dataflow2) has connectors for sources such as [XML](https://learn.microsoft.com/en-us/power-query/connectors/xml) (hint hint) + +## Tips + +- Sometimes Spark is the best tool for the job, but sometimes it's not. Don't be afraid to use more than one tool in the Fabric platform in your solution. Or try solving the same problem with different tools (e.g., notebook and a dataflow). + +## Too comfortable? Eager to do more? Try an additional challenges! + +- You've probably guessed that the weather and water conditions are quite important to divers. Climate models are a great way to look at future predictions, and there are a number of open datasets available. Try adding a climate model to your data for both weather and wave conditions. (Hint: Microsoft Planetary Computer ECMWF) +- OneLake can also store files, not just tables. Maybe your silver dataset can include other unstructured data such as images that would be useful for your data story? diff --git a/067-FabricLakehouse/Student/Challenge-04.md b/067-FabricLakehouse/Student/Challenge-04.md new file mode 100644 index 0000000000..fb48e68feb --- /dev/null +++ b/067-FabricLakehouse/Student/Challenge-04.md @@ -0,0 +1,28 @@ +# Challenge 04 - Make a Splash! + +[< Previous Challenge](./Challenge-03.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-05.md) + +## Introduction + +You've found your data, cleaned combined and shaped it into something useful. Now it's time to create your data story! + +## Description + +Remember the two main criteria? Boost customer engagement through data storytelling and enable assessing the diving conditions both beforehand and on-board. Now we're going to really make a splash with Margie's Travel by bringing your findings to life. + +## Success Criteria + +A Power BI report is an obvious solution, but you are free to wow our customer with any other creative way of presenting your findings. Interpretive dance anyone? + +To complete this challenge successfully, you should: + +- Create a report, or other way to portray your findings in a compelling way. Augment your data with additional sources if you wish, such as photos or links to other resources (but please remember to respect copyright!) +- Remember your solution should meet the needs of two distinct audiences: customers and dive crew - the data may be the same, but the presentation might not be. +- You will have 10 minutes to present your solution so make it impactful and succinct! + +## Tips + +- You can use any format, but a Power BI report is an obvious choice. +- Don't go "overboard" with the PowerPoint. +- A live demo always makes a splash! +- Enrich your pieces of eight with some gold doubloons! Add some additional data sources to your lakehouse to make your data story even more compelling. diff --git a/067-FabricLakehouse/Student/Challenge-05.md b/067-FabricLakehouse/Student/Challenge-05.md new file mode 100644 index 0000000000..a6044c1b23 --- /dev/null +++ b/067-FabricLakehouse/Student/Challenge-05.md @@ -0,0 +1,21 @@ +# Challenge 05 - Giant Stride + +[< Previous Challenge](./Challenge-04.md) - **[Home](../README.md)** + +## Introduction + +You've built a compelling data story! Now it's time to present your findings. + +## Description + +A good hack always wraps up with a retro, and this hack is no different. You will present your findings to Margie's Travel (i.e., the rest of the group) and discuss what you've learned. + +You have 10 mins to present your story. You can use any format you like, but you should be able to demonstrate your data story in action. You should also be prepared to answer questions from the audience. + +## Success Criteria + +We'll leave this one up to the rest of the group to decide! + +## Tips + +Remember to tell the story, not just the technology. diff --git a/067-FabricLakehouse/Student/Resources/.gitkeep b/067-FabricLakehouse/Student/Resources/.gitkeep new file mode 100644 index 0000000000..e69de29bb2 diff --git a/067-FabricLakehouse/Student/images/WTH.png b/067-FabricLakehouse/Student/images/WTH.png new file mode 100644 index 0000000000..fc54504eb7 Binary files /dev/null and b/067-FabricLakehouse/Student/images/WTH.png differ diff --git a/067-FabricLakehouse/Student/images/upload-all.png b/067-FabricLakehouse/Student/images/upload-all.png new file mode 100644 index 0000000000..1254c79fb1 Binary files /dev/null and b/067-FabricLakehouse/Student/images/upload-all.png differ diff --git a/067-FabricLakehouse/Student/images/upload-folder.png b/067-FabricLakehouse/Student/images/upload-folder.png new file mode 100644 index 0000000000..3eff68f2be Binary files /dev/null and b/067-FabricLakehouse/Student/images/upload-folder.png differ diff --git a/067-FabricLakehouse/Student/images/upload-raw.png b/067-FabricLakehouse/Student/images/upload-raw.png new file mode 100644 index 0000000000..1f8efcbceb Binary files /dev/null and b/067-FabricLakehouse/Student/images/upload-raw.png differ diff --git a/067-FabricLakehouse/Student/images/workspace-advanced-settings.png b/067-FabricLakehouse/Student/images/workspace-advanced-settings.png new file mode 100644 index 0000000000..0068a731a0 Binary files /dev/null and b/067-FabricLakehouse/Student/images/workspace-advanced-settings.png differ