AlloyDB reads Iceberg tables in place and joins them with live operational data, using the same copy as the rest of your lakehouse.
Iceberg support is built into AlloyDB and is accelerated by the AlloyDB columnar engine, so queries over lakehouse data are fast. They run on the same database instance as your application, without a separate reader instance to manage, and they read the latest data your team has published to the lakehouse.
In our testing on a 1 TB dataset in Cloud Storage, AlloyDB direct Iceberg access is up to 7x faster on aggregation queries than Amazon’s comparable PostgreSQL-compatible service, and up to 3.5x faster on aggregation queries than the open-source PostgreSQL extension.
AlloyDB sync for the lowest-latency reads (GA)
Not every table needs to be read in place. A retail merchant risk score, for example, is checked before every payment but only changes when the data team recomputes it overnight. Data like this, read on every request and refreshed on a schedule, is fastest as a local table in AlloyDB. The challenge is keeping that copy fresh without building and maintaining a pipeline.
AlloyDB sync copies lakehouse data from BigQuery into AlloyDB, once or on a schedule, and keeps it refreshed. The copy is a local AlloyDB table, so your app reads it as fast as any other table, and you can index it, serve it from read pools, or use it with the columnar engine and vector search. You can start a sync with one click from the BigQuery console or from SQL:
