Reverse ETL syncs high-quality data from a lakehouse into the operational systems that power applications. This ensures that trusted datasets and AI-driven insights flow directly into applications that power personalization, recommendations, fraud detection, and real-time decisioning.
Without Reverse ETL, insights remain in the lakehouse and don’t reach the applications that need them. The lakehouse is where data gets cleaned, enriched, and turned into analytics, but it isn’t built for low-latency app interactions or transactional workloads. That’s where Lakebase comes in, delivering trusted lakehouse data directly into the tools where it drives action, without custom pipelines.
In practice, reverse ETL typically involves four key components, all integrated into Lakebase:
- Lakehouse: Stores curated, high-quality data used to drive decisions, such as business-level aggregate tables (aka “gold tables”), engineered features, and ML inference outputs.
- Syncing pipelines: Move relevant data into operational stores with scheduling, freshness guarantees, and monitoring.
- Operational database: Optimized for high concurrency, low latency, and ACID transactions.
- Applications: The final destination where insights become action, whether in customer-facing applications, internal tools, APIs, or dashboards.
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