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How To Start A Data Engineering Project - With Data Engineering Project Ideas

Seattle Data Guy 28,928 lượt xem 3 years ago
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When you look to build a data engineering project there are a few key areas you should focus on.

- Multiple Types Of Data Sources(APIs, Webpages, CSVs, JSON, etc)
- Data Ingestion
- Storing Data
- Data Visualization (So you have something to show for your efforts).
- Cloud provider

Here is another list of project ideas if you need more inspiration
https://www.theseattledataguy.com/5-data-engineering-projects-to-add-to-your-resume/#page-content


I also reference two projects in this video, here are links to them
https://www.startdataengineering.com/post/data-engineering-project-for-beginners-batch-edition/

https://www.sspaeti.com/blog/data-engineering-project-in-twenty-minutes/

If you enjoyed this video, check out some of my other top videos.

Top Courses To Become A Data Engineer In 2022
https://www.youtube.com/watch?v=kW8_l57w74g

What Is The Modern Data Stack - Intro To Data Infrastructure Part 1
https://www.youtube.com/watch?v=-ClWgwC0Sbw

Also, consider checking out the GCP Data Engineering Certificate
coursera.pxf.io/BXYazy

Video Outline
0:00 Intro
0:53 What Data Engineering Tools Should You Use
3:44 What Data Sets Should You Use For Your Data Engineering Project
5:05 What Is Your End Goal
8:10 3 Data Engineering Project Ideas

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Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio

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About me:
I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

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