Build Real-time Applications with RisingWave and Kafka
Transform Your Streaming Data into Actionable Insights.
Trusted by 1,000+ Data-Driven Organizations
for Real-time Analytics
Trusted by 1,000+ Data-Driven Organizations for Real-time Analytics
Real-time Data Processing
RisingWave is ideal for scenarios where immediate data responsiveness is crucial. It extends the potential of Kafka, making it indispensable for industries demanding precision and agility, such as financial services, real-time monitoring, and Internet of Things (IoT) applications.
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Stream Processing like Never Before
Stream Processing as Easy as PostgreSQL
Apply your team’s PostgreSQL expertise to execute complex streaming joins and advanced time windowing. This compatibility extends to client libraries, ensuring seamless integration with existing PostgreSQL tools and applications.
Fully Managed in RisingWave Cloud
Seamlessly integrate data from any Kafka flavor with RisingWave. Automatically handle catalog extraction and transformation management to power reactive, real-time applications.
Unified Streaming & Batch Processing
Break down data silos with one platform that handles both stream and batch processing. Continuously process streaming data alongside application data in databases and build critical insights.
End-to-End Streaming with Kafka and Beyond
RisingWave integrates effortlessly with any Kafka flavor—whether it’s Redpanda, Pulsar, Confluent Kafka, or MSK, expect smooth streaming experience from day one. Bring your own Kafka, and RisingWave will automatically manage catalog extraction and transformation. No Kafka? We offer tailored onboarding to help you choose and set up the best option.
Powerful Streaming Processing Capabilities
Processing streams in Kafka exposes gaps like limited advanced processing, manual state management, and complex scaling. RisingWave addresses these gaps, offering a comprehensive streaming experience to handle multi-stream joins, temporal data, and schema evolution effortlessly.
Continuous Processing in SQL
Embrace full set of streaming SQL features, including watermarking, time windowing, and temporal filtering, build sophisticated stream processing and real-time analytics at ease.
High-Performance With Consistency Guarantees
Handling complex queries at high-performance with consistency guaranteed. RisingWave’s decoupled compute and storage architecture allows for the efficient joining of multiple data streams and historical data without concern for join state size
Dynamic Scaling and Instant Failure Recovery
Scale up or down within seconds, ensuring efficient handling of varying workloads without manual intervention. Built for resilience — recover from disruptions in seconds, minimize downtime while maintaining data consistency and availability
Hands-Free State and Schema Management
RisingWage ensures that the system can adapt seamlessly to evolving data requirements. Allow teams to modify their data stream schemas without downtime.
Exactly-Once Processing
RisingWave provides a consistency guarantee over the consumption of streams, ensuring no unexpected data loss or duplication even after a failure recovery.
Serverless Backfilling
Efficiently backfill historical data without long wait times. This capability is particularly useful in environments where schema changes or business logic updates occur frequently
RESOURCES
We’ve Got Lots of Helpful Tips
and Resources for You
[ blog ]
You deployed your streaming SQL pipelines to production. The materialized views are running, data is flowing, and your dashboards look great. Then something changes. A view falls behind. Query latency spikes. Storage grows faster than expected. How d...
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[ blog ]
Your product manager asks how revenue looked in the last five minutes. Your data engineer says the warehouse refreshes overnight. Your SRE points at a Grafana dashboard that shows infrastructure metrics but nothing about the business. Sound familiar?...
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[ blog ]
Joining two tables in a batch database is straightforward. Both tables sit on disk, the query planner picks a join strategy, and you get your result. But what happens when both sides of the join are continuously changing streams?
Streaming joins intr...
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