Databricks lakehouse patterns, Unity Catalog, Delta-to-Iceberg migration, and performance engineering guides.
6 articles
A deep technical comparison of Apache Iceberg and Delta Lake across metadata, schema evolution, partitioning, engine support, and operations — and how a control plane closes Iceberg's operational gap.
Databricks to Iceberg smooth migration opens a multi-engine lakehouse — not a platform exit. Databricks stays central for ML and Spark; Iceberg adds Trino, Snowflake, and open catalogs. Five tools: LakeOps, UC managed Iceberg, Delta UniForm, Spark, and Lakehouse Federation.
A technical comparison of the seven major Apache Iceberg catalogs — Hive Metastore, AWS Glue, Apache Polaris, Project Nessie, Databricks Unity Catalog, Apache Gravitino, and Lakekeeper — across protocol support, access control, multi-engine interoperability, credential vending, and production readiness.
Data lakes, warehouses, and lakehouses are not interchangeable — each has hard limits the others cannot cover. A practical guide for platform leaders: where each architecture wins, where it fails, cost and governance trade-offs, and how to choose (or combine) them in 2026.
For a decade, Snowflake and Databricks defined enterprise data. Then the lakehouse emerged — open formats on open storage. What was missing was the operational layer to make it work at scale. An autonomous control plane turns a lakehouse into a managed open data platform — without the lock-in.
A detailed comparison between Delta Lake and Apache Iceberg, exploring their architectures, performance characteristics, and ideal use cases to help you make the right choice.
