OLake is a high-performance, open-source data ingestion engine for replicating databases, S3, and Kafka into Apache Iceberg (or plain Parquet).
Built for scalable, real-time pipelines, OLake provides a simple web UI and CLI - used to ingest into vendor-lock-in free Iceberg tables supporting all the query-engines/warehouses.

Read the docs and benchmarks at olake.io/docs. Join our active community on Slack.

OLake supports replication from transactional databases such as PostgreSQL, MySQL, MongoDB, Oracle, DB2, and MSSQL, event-streaming systems like Apache Kafka and Object-store like S3, into open data lakehouse formats such as Apache Iceberg or Plain Parquet — delivering blazing-fast performance with minimal infrastructure cost.

• 🧠 Smart sync: Full + CDC replication with automatic schema discovery & schema evolution
• ⚡ High throughput: 580K RPS (Postgres) & 338K RPS (MySQL)
• ➡️ Exactly once delivery & Arrow writes: Accuracy with speed.
• 💾 Iceberg-native: Supports Glue, Hive, JDBC, REST catalogs
• 🖥️ Self-serve UI: Deploy via Docker Compose and sync in minutes
• 💸 Infra-light: No Spark, no Flink, no Kafka, no Debezium
• 🗜️ Iceberg Table Optimization (Coming soon): Compaction tailored for CDC ingestion

*These are preliminary results. Fully reproducible benchmark scores will be published soon.

1. Apache Iceberg Docs

   1. Catalogs
      1. AWS Glue Catalog
      2. REST Catalog
      3. JDBC Catalog
      4. Hive Catalog
   2. Azure ADLS Gen2
   3. Google Cloud Storage (GCS)
   4. MinIO (local)
   5. Iceberg Table Management
      1. S3 Tables Supported

2. Parquet Writer

   1. AWS S3 Docs
   2. Google Cloud Storage (GCS)
   3. Local FileSystem Docs

OLake UI is a web-based interface for managing OLake jobs, sources, destinations, and configurations. You can run the entire OLake stack (UI, Backend, and all dependencies) using Docker Compose. This is the recommended way to get started. Run the UI, connect your source DB, and start syncing in minutes.

curl -sSL https://raw.githubusercontent.com/datazip-inc/olake-ui/master/docker-compose.yml | docker compose -f - up -d

Access the UI: * OLake UI: http://localhost:8000 * Log in with default credentials: admin / password.

Detailed getting started using OLake UI can be found here.

With the UI running, you can create a data pipeline in a few steps:

1. Create a Job: Navigate to the Jobs tab and click Create Job.
2. Configure Source: Set up your source connection (e.g., PostgreSQL, MySQL, MongoDB).
3. Configure Destination: Set up your destination (e.g., Apache Iceberg with a Glue, REST, Hive, or JDBC catalog).
4. Select Streams: Choose which tables to sync and configure their sync mode (CDC or Full Refresh).
5. Configure & Run: Give your job a name, set a schedule, and click Create Job to finish.

For a detailed walkthrough, refer to the Jobs documentation.

For advanced users and automation, OLake's core logic is exposed via a powerful CLI. The core framework handles state management, configuration validation, logging, and type detection. It interacts with drivers using four main commands:

• spec: Returns a render-able JSON Schema for a connector's configuration.
• check: Validates connection configurations for sources and destinations.
• discover: Returns all available streams (e.g., tables) and their schemas from a source.
• sync: Executes the data replication job, extracting from the source and writing to the destination.

Find out more about CLI here.

Below are other different ways you can run OLake:

1. OLake UI (Recommended)
2. Kubernetes using Helm
3. Standalone Docker container
4. Airflow on EC2
5. Airflow on Kubernetes

1. OLake + Apache Iceberg + REST Catalog + Presto
2. OLake + Apache Iceberg + AWS Glue + Trino
3. OLake + Apache Iceberg + AWS Glue + Athena
4. OLake + Apache Iceberg + AWS Glue + Snowflake
5. OLake + Apache Iceberg + REST Catalog + Spark

• ✅ Migrate from OLTP to Iceberg without Spark or Flink
• ✅ Enable BI over fresh CDC data using Athena, StarRocks, Trino, Presto, Dremio, Databricks, Snowflake and more!
• ✅ Build near real-time data lake-house on cost-efficient cloud object stores
• ✅ Move away from vendor-lock-in warehouse or tools with open data lake-house
• ✅ Single copy for both analytics & machine learning.

• Oracle Full Load Support
• Oracle Incremental
• Filters for Full Load and Incremental
• Compaction & other table optimisations (In-progress)
• Iceberg V3 Support

📌 Check out our GitHub Project Roadmap and the Upcoming OLake Roadmap to track what's next. If you have ideas or feedback, please share them in our GitHub Discussions or by opening an issue.

We ❤️ contributions, big or small!

Check out our Bounty Program. A huge thanks to all our amazing contributors!

• To contribute to the OLake core, see CONTRIBUTING.md.
• To contribute to the UI, visit the OLake UI Repository.
• To contribute to our website and documentation, visit the Olake Docs Repository.