Explainers and opinions on Apache Iceberg, CDC and the lakehouse.
Equality deletes, position deletes, and deletion vectors compared for CDC pipelines: what each stores, which query engines can read them, what compaction fixes, and the table properties to set explicitly.
The Iceberg interoperability myth: how row-level delete support can leave a table readable in Spark but not in Snowflake, on the same catalog and data.
Same Iceberg table, same catalog, same data yet Spark reads it fine while Databricks fails. See what query-engine gaps break cross-engine interoperability.
OLake Go architecture deep dive: eight sources, one sync engine, chunked parallel backfills, native CDC, state and the Arrow write path to Iceberg.
OLake Go schema evolution into Apache Iceberg: automatic column adds, retained drops, safe type promotions, and failures only where data would corrupt.
How OLake Go delivers exactly-once data into Apache Iceberg across Full Refresh, Incremental and CDC syncs, using atomic commits and checkpoint recovery.