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Fastest way to replicate your data from
Database→Data Lakehouse
Get the OLake Advantage
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Experience the most
seamless workflow
Exclusively for Apache IcebergBuilt on Iceberg.
Born for Scale.
Schema evolution
Apache Iceberg enables seamless schema evolution by supporting column additions, deletions, renames, and reordering ensuring reliable analytics on evolving datasets without rewriting historical data.
Schema datatype changes
Apache Iceberg enables safe and forward-compatible data type evolutions. This guarantees robust schema evolution without the need to rewrite existing data or disrupt downstream queries.
Partitioning and partition evolution
Apache Iceberg supports flexible partitioning without requiring data to be physically rewritten. Partition evolution allows you to safely change partition strategies over time without impacting existing data.
Get the bestwith OLake
| Metrics |  OLakeOpen Source |  Airbyte |  Fivetran |  Debezium |  Estuary |
|---|---|---|---|---|---|
| Rows synced | 4.01 Billion | 12.7 Million | 4.01 Billion | 1.28 Billion | 0.34 Billion |
| Elapsed time | 4.7 hours | 7.5 hours (failed sync) | 24 hours | 24 hours | 24 hours |
| Speed (Rows/Sec) | 2,35,411 RPS | 457 RPS | 46,395 RPS | 14,839 RPS | 3,982 RPS |
| Comparison | - | 515.1 × slower | 5.1× slower | 15.9 × slower | 59.1 × slower |
| Cost | $ 15 | $ 5,560 | $ 0 (Free full load) | $ 75 | $1,668 |
We know how to stand out
Faster Resumable Full Load
Full load performance is improved by splitting large collections into smaller virtual chunks, processed in parallel.
Schema-Aware Logs and Alerts for Integrity
Actively monitors sync failures, schema changes, and data type modifications, ensuring that issues like incompatible updates or ingestion errors are swiftly detected, clearly logged, and immediately surfaced through real-time alerts
CDC Cursor Preservation
When you add new big tables after a long time of setting up the ETL, we do full load for it, in parallel to already running incremental sync. So CDC cursors are never lost. We manage overhead of data ingestion order and deduplication.
Achieve near real-time latency
Using Databases change stream logs (binglogs for MySQL, oplogs for mongoDB, WAL logs for Postgres), OLake enables parallel updates for each collection. This method facilitates rapid synchronization and ensures that data is consistently updated with near real-time updates.
Fast & Efficient
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How to Set Up PostgreSQL to Apache Iceberg Replication for Real-Time Analytics: Complete Guide
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In recent years, terms such as deletion vectors, position deletes, and other related concepts have become increasingly common in discussions around modern data lakehouse technologies...