Metadata Platforms in 2026: DataHub, OpenMetadata, Atlan, and Catalog Convergence
How the technical catalog and the metadata platform are converging in 2026, and how to arrange the two layers for a lakehouse.
The archive
652 posts on Apache Iceberg, lakehouse architecture, data engineering and applied AI.
How the technical catalog and the metadata platform are converging in 2026, and how to arrange the two layers for a lakehouse.
Why moving Iceberg tables between catalogs is a pointer copy, and the protocol that makes a cutover safe for one table or thousands.
Building an OpenTelemetry data lake on Iceberg: schemas for spans, logs, and metrics, ingestion, query patterns, and retention.
Where Airflow 3, Dagster, Prefect, and event-driven triggering stand for lakehouse pipelines in 2026, after the Prefect acquisition of Dagster.
What pg_lake, pg_duckdb, and pg_mooncake do at the Iceberg level, and honest thresholds for when Postgres is enough.
How Avro, Protobuf, and JSON Schema evolve through a registry, and how that maps to the schema evolution rules of Iceberg.
How the spec-defined bucket transform lets engines skip the shuffle in joins, and how to set it up and keep it engaged in Spark.
What synthetic data in a lakehouse is for, the generation methods, how to preserve fidelity, and where synthetic tables belong.
What the evidence shows about whether AI replaces data engineers, which parts of the job compress, and which parts do not.
A background agent can move cold Iceberg partitions to cheaper tiers without breaking live queries. Heatmaps, path-safe moves, and restore paths.