Data Platform

Most organisations still run separate data, integration, and AI teams — three roadmaps, three toolchains, three reporting lines. That separation made sense when each function had genuinely distinct skills. It doesn't hold up as well now. Here's the case for a converged data team, and what it should actually look like.

Three genuinely different mechanisms exist for row and column level security in Unity Catalog. A practitioner's guide to what each one is actually for — and the one mistake that trips up more rollouts than any misconfigured filter

A CDF history table captures every Delta Lake change permanently — independent of VACUUM retention. Here's the pattern, the schema evolution handling, and the partitioning strategy that scales

SCD Type 2 Databricks implementation compared — manual MERGE logic versus Lakeflow's APPLY CHANGES INTO. Working code, gotchas, and migration lessons from real production pipelines

Change Data Feed in Databricks looks simple in the documentation. In production, it breaks in six distinct ways — and each one is silent until something downstream stops working. This is what I learned building CDF pipelines on Azure Databricks.

If your business runs on more than five software platforms, your analysts spend more time collecting data than analysing it, or your reports tell different stories depending on which system you look at — this guide was written for you.