DEW #133: How to Implement Write-Audit-Publish (WAP), Vector Database - Concepts and examples & Data Warehouse Testing Strategies for Better Data Quality

Welcome to another episode of Data Engineering Weekly. Aswin and I select 3 to 4 articles from each edition of Data Engineering Weekly and discuss them from the author’s and our perspectives.

On DEW #133, we selected the following article

LakeFs: How to Implement Write-Audit-Publish (WAP)

I wrote extensively about the WAP pattern in my latest article, An Engineering Guide to Data Quality - A Data Contract Perspective. Super excited to see a complete guide on implementing the WAP pattern in Iceberg, Hudi, and of course, with LakeFs.

https://lakefs.io/blog/how-to-implement-write-audit-publish/

Jatin Solanki: Vector Database - Concepts and examples

Staying with the vector search, a new class of Vector Databases is emerging in the market to improve the semantic search experiences. The author writes an excellent introduction to vector databases and their applications.

https://blog.devgenius.io/vector-database-concepts-and-examples-f73d7e683d3e

Policy Genius: Data Warehouse Testing Strategies for Better Data Quality

Data Testing and Data Observability are widely discussed topics in Data Engineering Weekly. However, both techniques test once the transformation task is completed. Can we test SQL business logic during the development phase itself? Perhaps unit test the pipeline?

The author writes an exciting article about adopting unit testing in the data pipeline by producing sample tables during the development. We will see more tools around the unit test framework for the data pipeline soon. I don’t think testing data quality on all the PRs against the production database is not a cost-effective solution. We can do better than that, tbh.

https://medium.com/policygenius-stories/data-warehouse-testing-strategies-for-better-data-quality-d5514f6a0dc9

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