Data Vault articles

4-day workshop Β· In-person or online

What would it take for you to trust your Databricks pipelines in production?

A 3-day bug hunt on a 3-person team costs up to €7,200 in lost engineering time. This workshop teaches you to prevent that β€” unit tests, data tests, and integration tests for PySpark and Databricks Lakeflow, including Spark Declarative Pipelines.

Unit, data & integration tests
Medallion architecture & Lakeflow SDP
Max 10 participants Β· production-ready templates
See the full curriculum β†’ €7,000 flat fee Β· cohort of up to 10
Bartosz Konieczny
Bartosz
Konieczny

Data modeling with Data Vault - part 1

If you hear "agile", "adapted to the changes", you certainly think about Scrum, Kanban and generally the Agile methodology. And you're correct but it's worth knowing that the agile term also applies to the data. More exactly, to the data modeling with the approach called Data Vault.

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Data Vault 2.0 and Big Data

In the previous blog post you discovered the first version of Data Vault methodology. But since the very first iteration, the specification evolved and a few years ago a version 2 was proposed. More adapted to the Big Data world, with several deprecation notes, and more examples adapted to the constantly evolving data world.

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