Continuous sync for accelerated workloads
Automatically capture and apply changes from databases, event logs, and other data sources directly into accelerated datasets, delivering up-to-date data without streaming pipelines.
Do more with your data
Consolidate operational, analytical, and object store data into a governed and accelerated SQL query engine.
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up to 100x faster queries
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up to 80% cost savings on data lakehouse spend
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increase in data reliability for critical workloads
Real-time dataset updates
Stream changes from distributed data sources directly into Spice in real time. Only modified data is transferred, keeping accelerated datasets like DuckDB and SQLite synchronized.
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Optimized for acceleration
Combine CDC with persistent acceleration engines for sub-second queries on live data. Spice resumes from the last known state after restart, avoiding full reloads while maintaining strong consistency and performance.
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Built for event-driven workloads
Spice CDC powers real-time AI and analytics for use cases like fraud detection, personalization, and IoT-so applications always work with the freshest data.
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Integrations across your data and observability stack
Accelerate your data stack with a library of 40+ prebuilt connectors for the most common databases, warehouses, and object stores-from Databricks and S3 to MySQL and PostgreSQL.
Do more with your data
Run data-intensive workloads on a high-performance data runtime trusted by teams building real-time systems at scale.
“Spice opened the door to take these critical control-plane datasets and move them next to our services in the runtime path.”
Peter Janovsky
Software Architect, Twilio
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Faster queries
“It just spins up and works, which is really nice. The responsiveness is amazing, which is a huge gain for the customer.”
Darin Douglass
Principal Software Engineer, Barracuda
“What I like the most about Spice is that it's very easy to collect data from different data sources, and I'm able to interact with this data and do everything in one place.”
Dustin Warner
Director of Software Engineering, NRC Health
FAQs
Answers to common questions about change data capture in Spice
How does change data capture keep datasets in sync?
Spice captures inserts, updates, and deletes from databases, event logs, and other data sources, and applies them directly to accelerated datasets as they occur. Only modified data is transferred, so datasets stay current without full-table refreshes or separate streaming pipelines.
Do I need Kafka or Debezium to use CDC with Spice?
No. CDC is built into the Spice runtime, so accelerated datasets stay synchronized with source databases without Kafka, Debezium, or separate streaming infrastructure for many use cases. You enable it by configuring a change-based refresh mode on an accelerated dataset.
What happens to CDC when Spice restarts?
Spice resumes from the last known state after a restart, avoiding full reloads while maintaining strong consistency and performance. Combined with persistent acceleration engines, this keeps sub-second queries running on live data.
Which acceleration engines can CDC keep updated?
CDC keeps persistent accelerated datasets such as DuckDB and SQLite synchronized with their sources. This pairs with data lake acceleration to serve fast queries over continuously changing data.
What kinds of applications benefit from real-time CDC?
Event-driven workloads such as fraud detection, personalization, and IoT benefit most because they depend on the freshest data. CDC also keeps datasets current for AI applications like retrieval-augmented generation, where responses are grounded in live data.
See Spice in action
Walk through your use case with an engineer and see how Spice handles federation, acceleration, and AI integration for production workloads.
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