Databricks unifies OLTP and OLAP, depending on what counts as a copy
When Databricks claimed to have cracked an age-old database problem, it came with a clear marketing message: "One data, zero compromises, zero copies." Inevitably, that led engineers to search for clarity. After all, the company claimed to have unified OLTP and OLAP with "no data duplication."
Databricks, which was founded around the open source unified analytics engine Apache Spark, called its invention LTAP, which stands for lake transactional/analytical processing. It works with Reyden – a new compute engine – and Lakebase, its serverless PostgreSQL on open object storage.
Databricks is attempting to address a fundamental database challenge. OLTP (online transactional processing) performs small, row-oriented reads and frequent writes, while OLAP (online analytical processing) performs large, column-oriented reads and batch writes. Down to the physical level, it is challenging to get the two to coexist in a single system. The issue is seen as more pressing now as the database...
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