Use Cases for OLake

1. Offloading OLTP Databases for Analytics

Running complex analytical queries directly on production OLTP (Online Transaction Processing) databases can degrade performance and affect transactional workloads.

OLake addresses this by replicating data from MySQL, PostgreSQL, Oracle, and MongoDB into an Apache Iceberg based data lake.

This approach provides:

• Workload separation → Keep analytics independent from production databases.

• Faster performance → Run queries on Iceberg tables optimized for analytics.

• Efficient bootstrapping → Use full-load sync for initial data migration.

• Resilience → Dead Letter Queue (DLQ) ensures schema changes don’t break pipelines.

With OLake, you can maintain stable transactional systems while enabling scalable and reliable analytics on Apache Iceberg.

2. Building Open Data Stacks and Scaling Data Engineering

Organizations looking to reduce reliance on proprietary ETL and data warehousing tools can use OLake as part of an open-source data stack. By standardizing on Apache Iceberg as the table format, OLake ensures broad compatibility with query engines like Trino, Presto, Spark, Dremio, and DuckDB.

With its open-source approach, OLake helps teams:

• Replace managed ETL/replication services with a community-driven alternative.

• Customize and extend the platform through its open codebase.

• Standardize data lake storage on Apache Iceberg.

• Avoid vendor lock-in tied to closed formats and tools.

Support multiple query engines across different use cases and teams.

This enables a flexible, scalable, and future-proof data architecture built on open standards.

3. Enabling Near-Real-Time Analytics

Modern applications need fresh data within minutes, not hours. OLake enables near-real-time analytics by continuously replicating data from transactional databases using log-based CDC, often achieving sub-minute latency for updates to appear in Iceberg.

Key benefits:

• Low-latency ingestion with log-based CDC.

• Incremental processing powered by Apache Iceberg.

• Efficient queries using Iceberg optimizations like hidden partitioning, metadata pruning,

This allows teams to run fast, cost-efficient analytics on frequently updated data.

4. Cost-Effective Data Retention and Compliance

Storing historical data for compliance, audits, or analysis can be costly in traditional data warehouses. OLake addresses this by replicating data into Apache Iceberg, which stores it on cost-efficient object storage (e.g., S3, GCS).

With Iceberg, data remains immediately queryable across compatible engines—no rehydration needed. Its built-in schema evolution ensures that structural changes over time don’t break access to historical data.

Key benefits:

• Store large historical datasets at lower cost.

• Keep retained data instantly queryable.

• Meet compliance and auditing needs with long-term retention.

Adapt to schema changes seamlessly with Iceberg.

5. Powering AI and ML Data Pipelines

Building effective AI and ML models requires fresh, reliable, and structured data. OLake automates the ingestion of transactional data into an Iceberg-based lakehouse, ensuring that pipelines always have access to the latest information.

With continuous updates, ML feature stores and training datasets stay current, while Iceberg’s compatibility with engines like PySpark and DuckDB makes it easy to plug into existing data science workflows. This supports faster model development and iteration.

Key benefits:

• Automate ingestion of transactional data for ML feature stores.

• Provide consistent, up-to-date data for training and evaluation.

• Seamlessly integrate with ML processing engines (e.g., PySpark, DuckDB).

6. Simplifying Change Data Capture

Setting up scalable CDC pipelines is often complex. OLake makes this easier by providing an open-source solution purpose-built for database-to-Iceberg replication.

It uses log-based CDC for minimal impact on source databases, supports schema evolution to handle structural changes, and includes a Dead Letter Queue (DLQ) for reliable error handling. The design aligns with open-source streaming concepts, ensuring flexibility and robustness.

Key benefits:

• Log-based CDC with low impact on source systems.

• Automatic handling of schema changes.

• Dead Letter Queue for dependable error management.

7. Reducing Cloud Data Warehouse Costs

Cloud data warehouses can become expensive due to storage and compute costs. OLake helps reduce these expenses by offloading raw, historical, or less frequently used data into an Iceberg lakehouse on cost-effective object storage.

This lets teams keep their warehouse optimized for active data, while still retaining full access to complete datasets in Iceberg.

Key benefits:

• Lower costs by storing large datasets in Iceberg on object storage.

• Optimize warehouse usage for critical, high-value data.