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Mentions
How do you handle SQL queries across lakehouse data and different compute engines? I've been looking into an open-source project called Open Lakehouse Compute Fabric (OLCF), and one part that caught my attention is how it brings SQL and lakehouse workflows together. It includes a SQL editor, DuckDB for querying, Spark SQL, and support for working with lakehouse data stored in formats such as Delta Lake and Parquet. It also has features for exploring tables, inspecting data, and building dashboards from query results. What I find interesting is that SQL is part of a broader platform that also includes Python notebooks and data pipelines, rather than being treated as a separate tool. The project is still in beta, so I'm curious how its approach holds up for different workloads and data setups. Here's the GitHub repository for anyone interested in the implementation: jddevtime/open-lakehouse-compute-fabric For those working with SQL and lakehouse systems, how do you usually handle queries across different storage formats and compute engines? Do you prefer keeping SQL tools separate, or having them integrated into one platform?