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Use cases

Analytical Offload

CrateDB offloads analytical queries from transactional databases and operational systems. By replicating or streaming data into CrateDB, teams protect production performance while enabling complex, high-volume analytics without impacting critical workloads.

Production databases are designed to run applications, not to support heavy analytical queries at scale. As data volumes and analytics demands grow, running complex reports and aggregations directly on operational systems can degrade performance, increase costs, and introduce risk.

Analytical offload separates analytics from production workloads, allowing teams to analyze operational data freely while keeping critical systems fast and stable.

Offload Heavy Queries Safely

CrateDB absorbs analytical workloads that would otherwise overload transactional databases. Complex queries run on a dedicated analytics layer, ensuring consistent application performance and system reliability.

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Analyze Operational Data at Scale

Once data is offloaded, teams can run large aggregations, historical analyses, and multi-dimensional queries without the constraints of OLTP systems. Analytics scale independently, without impacting production traffic.
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Reduce Risk and Cost

By separating analytics from production workloads, organizations reduce operational risk and avoid over-provisioning transactional databases. This leads to better cost control and more predictable system behavior.

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Modernize Incrementally

CrateDB fits seamlessly into existing architectures. Teams can introduce analytics offload without disruptive migrations, major refactoring, or changes to application code.

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Where Traditional Systems Fall Short

Many organizations still run analytics directly on operational databases such as Postgres or MongoDB, or rely on complex pipelines to move data elsewhere. This often results in:

  • Slow queries: Analytical workloads interfere with transactional performance.
  • High costs: Scaling OLTP systems for analytics is inefficient and expensive.
  • Complex ETL pipelines: Moving data into warehouses or separate engines adds latency and overhead.
The result is delayed insights, frustrated teams, and rising infrastructure costs. CrateDB provides a safer, faster way to run analytics on operational data.
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Learn more about CrateDB
Video
From NoSQL to Real-Time Insights: Unlocking MongoDB Data with CrateDB CDC
From NoSQL to Real-Time Insights: Unlocking MongoDB Data with CrateDB CDC

From NoSQL to Real-Time Insights: Unlocking MongoDB Data with CrateDB CDC.

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Simon Prickett explaining the CrateDB Cloud Console with the MongoDB integration
From Data to Insights: Streaming Data from MongoDB to a Real-Time Analytics Database

Learn to integrate MongoDB with CrateDB Cloud using Change Data Capture, analyze data with SQL, and visualize updates in Grafana for real-time insights.

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Documentation
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Change Data Capture (CDC)

CrateDB Cloud enables continuous data ingestion from MongoDB using Change Data Capture (CDC). CrateDB also supports popular third-party data integration frameworks and platforms for CDC, both managed and unmanaged.

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Additional resources

FAQ

Analytical offload involves transferring resource-intensive analytical workloads from operational databases (like PostgreSQL or MongoDB) to a specialized system. This separation ensures that transactional systems remain responsive, while analytics can be performed efficiently and at scale.

CrateDB enables seamless replication from operational databases using Change Data Capture (CDC). This allows for real-time analytics on fresh data, supporting ad-hoc queries, dashboards, and AI workloads without impacting the performance of operational systems.

Yes. CrateDB can complement data warehouses by handling real-time analytics, while the warehouse focuses on batch processing and long-term storage. This hybrid approach optimizes performance and reduces costs.

Key benefits include:

  • Faster insights: Millisecond query responses.
  • Cost savings: Reduced need for scaling operational databases.
  • Operational stability: Uninterrupted performance of transactional systems.
  • Simplified architecture: Elimination of complex ETL processes.

CrateDB excels in scenarios requiring real-time analytics, high ingestion rates, and support for both structured, semi-structured and unstructured data. It's ideal for applications like IoT monitoring, log analysis, and real-time dashboards.