microsoft / microsoft/WhatTheHack
[Proposal]: Dive Into Fabric Lakehouse
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- Dominant language
- C#
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Description
Topic
This is a business problem solving focused hack, built around the narrative of expanding customer engagement for a small dive travel business using Microsoft Fabric.
A local dive travel company - Margie's Travel - have heard about Microsoft Fabric and are interested in how this could be used to reach existing and new customers. The brief revolves around using open data sources of climate and shipwreck data to provide dive conditions and dive site information for both trip planning and pre-dive briefings.
The hack is descriptive rather than prescriptive; example solutions for each challenge are provided as suggestions but students are free to approach problem solving in an open way.
Abstract/Learning Objectives
The learning outcomes of this hack are:
- Analysing and understanding customer requirements
- Researching publicly available datasets and assessing against requirements
- Landing data in OneLake
- Cleaning and ingesting data to Lakehouse using appropriate technology - spark, dataflows etc.
- Presenting the stories uncovered in the data to meet the customer requirements (Power BI, custom app etc)
- Playing back the solution to the customer
- Having fun!
Delivery Date
18/8/2023
Authors
No response
Other
No response
Code of Conduct
- I agree to follow this project's Code of Conduct
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files or tests are named. Start by turning the customer problem and learning objectives into a scoped hack brief, then review the climate and shipwreck data, OneLake/Lakehouse ingestion, Spark or dataflow, and presentation paths described here. Done means the hack has defined challenges, learner guidance, and a customer playback outcome.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- spark
- Domain
- data-engineering, data-visualization
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100