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Analytics Engineering

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Week 4 of Data Engineering Zoomcamp by @DataTalksClub complete

Just finished Module 4 - Analytics Engineering with dbt. Learned how to:

✅ Build transformation models with dbt
✅ Create staging, intermediate, and fact tables
✅ Write tests to ensure data quality
✅ Understand lineage and model dependencies
✅ Analyze revenue patterns across NYC zones

Transforming raw data into analytics-ready models - the T in ELT

Here's my homework solution: https://github.com/Derrick-Ryan-Giggs/Analytics-Engineering

Following along with this amazing free course - who else is learning data engineering?

You can sign up here: https://github.com/DataTalksClub/data-engineering-zoomcamp/

4 Comments

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Nice work, I didn’t realize how crucial testing and lineage were until you mentioned it, do you have a favorite dbt feature so far?

1 vote
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@[Greg Elfrink] Thanks Greg, I'd say incremental materializations are my favorite - they make processing 100M+ records actually feasible without breaking the bank on compute costs.

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Your insights on leveraging dbt for building transformation models really highlight the importance of data quality in analytics engineering, especially when analyzing complex data like NYC revenue patterns.

1 vote
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@[MasterCraft] Absolutely - the automated testing caught so many edge cases in the taxi data that would've caused major headaches downstream for analysts

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Derrick Ryan Giggs
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