Where Analytics Go Wrong
Many organizations still run analytics directly on operational databases like Postgres or MongoDB. The result is:
- Slow queries: Analytical workloads interfere with OLTP 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.
How CrateDB Fixes It
CrateDB enables organizations to separate analytics from operations while keeping everything in sync:
- Seamless replication: Change Data Capture (CDC) pipelines sync CrateDB with Postgres or MongoDB.
- Real-time analytics: Run ad-hoc queries, dashboards, and AI workloads directly in CrateDB.
- SQL-native: Works instantly with BI tools and applications.
- Elastic scale: Handle billions of rows and high concurrency with cost-efficient horizontal scaling.
The Results You Get
- Faster insights: Queries run milliseconds, not minutes or hours.
- Lower TCO: Eliminate costly OLTP scaling and reduce analytics infrastructure spend.
- Operational stability: Keep transactional systems responsive by offloading analytics.
- Simpler architecture: Remove complex ETL pipelines and multiple database dependencies.
How CrateDB relates to your data warehouse
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Data warehouses for batch analytics, reporting, and ML training.
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CrateDB for real-time dashboards, metrics, and alerts.
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Cost savings and better performance by focusing the warehouse on heavy batch jobs and CrateDB on sub-second queries on fresh data to reduce warehouse query load.
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Hybrid architecture: CrateDB for real time, the warehouse for long-term and historical analysis.
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