The data lakehouse architecture — combining the cost-efficiency of data lakes with the ACID guarantees of data warehouses — has become the dominant pattern for enterprise analytics at scale.
Choosing the right table format (Delta Lake, Apache Iceberg, or Apache Hudi) is one of the most consequential decisions a data team makes. Here's our experience building production lakehouses across 15+ enterprise clients.
The short version
If you're on Databricks, use Delta Lake. If you're on AWS Glue or running multi-engine (Spark + Flink + Trino), use Iceberg. If you need real-time upserts and low-latency ingestion, evaluate Hudi seriously.
Delta Lake: the safe default on Databricks
Delta Lake's tight integration with Databricks means you get first-class support for time travel, schema evolution, and performance optimization out of the box. The Delta Lake open source version is solid, but the Databricks managed version adds significant value through Delta Live Tables and automatic optimization.
Apache Iceberg: the multi-engine choice
Iceberg's strength is engine neutrality. You can write with Spark, query with Trino, and compact with Flink — all on the same table. This flexibility is valuable for organizations with diverse query workloads or those avoiding vendor lock-in.
Schema evolution: all three handle it, with different tradeoffs
All three formats support schema evolution (adding/dropping/renaming columns). Delta Lake's schema enforcement is strictest by default, which reduces data quality incidents. Iceberg's partition evolution is more flexible, allowing you to change partition strategies without rewriting data.