Databricks
Automatic change data feed GA: write faster, query-time CDC
Automatic change data feed (Auto CDF) GA shifts CDC from write time to query time. MERGE and UPDATE run 15% faster, but read costs move the other way.
#iceberg
8 stories tagged iceberg.
Snowflake
Snowflake Postgres data mirroring explained
Snowflake Postgres data mirroring continuously replicates Postgres tables into Snowflake using Iceberg with 30-second lag. It replaces external CDC connectors with serverless compute and open formats.
Databricks
Databricks managed Iceberg materialized views, explained
Managed Iceberg materialized views let external engines like Trino read your Databricks MVs. Now in Public Preview, they shift the lock-in calculus.
Snowflake
Dynamic Iceberg table replication, costed honestly
Dynamic Iceberg table replication now keeps Snowflake managed Iceberg pipelines in DR plans, but refresh and transfer costs move to the target account.
Snowflake
Snowflake Dynamic Tables refresh gets a cost test
Snowflake Dynamic Tables now claim up to 2.8x faster refresh on Gen2 warehouses, but the cost win depends on target lag and change volume.
Snowflake
Snowflake Iceberg ADLS writes are finally usable
Azure teams can now read and write externally managed Iceberg tables in Snowflake without moving storage, through GA ADLS support.
Snowflake
ICEBERG_MERGE_ON_READ_BEHAVIOR changes Iceberg DML
Set to AUTO, Snowflake's GA ICEBERG_MERGE_ON_READ_BEHAVIOR switch sends 3 of 4 Iceberg table cases to merge-on-read.
Snowflake
Should you put your data lake in Snowflake Iceberg tables?
Snowflake Iceberg tables store data in open Apache Iceberg format in your own cloud storage, so Snowflake bills zero storage and other engines can read it. The catch: only Snowflake-managed catalog tables get full platform support, external-catalog tables lose clustering, cloning, and replication.