Iceberg Tables
Open, scalable, and high-performance data lake architecture.
Enable modern data lakehouse capabilities with efficient storage, versioning, and query performance.
Lakehouse performance without complexity
Iceberg tables bring structure and reliability to large-scale data lakes. MonkDB enhances this with native, real-time integration.
What MonkDB makes possible for iceberg tables
Efficient handling of large analytical datasets
Petabyte-scale tables with high-performance scans.
Schema evolution and version control
Safe schema changes and time travel built into the table format.
High-performance querying across massive data volumes
Predicate pushdown, partition pruning, and vectorized execution.
Seamless integration with real-time data
Iceberg tables coexist with streaming and operational data in one engine.
Three steps, one continuous loop
Open lakehouse, no movement
Data lives in your object store as Iceberg tables. MonkDB reads it natively.
Schema and partition evolution
Time-travel, version history, and zero-downtime schema changes are first-class.
Query alongside live state
Historical lake data joins live tables in one SQL surface. No federation hop.
We kept our existing lake and added MonkDB on top. The same Iceberg tables now serve operational queries and analytics from one engine.
- 0Data migration required
- 5×Faster lake queries
- PBProduction scale
More ways to put MonkDB to work, in your domain
Lakehouse performance, without the lakehouse stack.
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