FalkorDB
FalkorDB is an ultra-fast, multi-tenant graph database designed to power Generative AI applications with optimized memory and linear scalability.
RisingWave is a blazing-fast SQL stream processing engine for mission-critical real-time applications, integrating streaming and lakehouse worlds.

RisingWave is a cloud-native streaming database designed for real-time data processing and analysis. Built in Rust, it combines sub-100 ms latency with built-in Apache Iceberg integration. It continuously ingests data from databases, message queues, and IoT devices, transforms and enriches it in motion using SQL, and materializes results for instant serving. RisingWave unifies streaming and lakehouse worlds, simplifying data pipelines for use cases like monitoring and alerting, real-time data enrichment, IoT and telemetry, and streaming lakehouse ingestion. It allows users to process, analyze, and persist data seamlessly in one place, offering a cost-effective and efficient solution for demanding event streaming workloads.
RisingWave is a cloud-native streaming database designed for real-time data processing and analysis.
Explore all tools that specialize in real-time data ingestion from various sources. This domain focus ensures RisingWave delivers optimized results for this specific requirement.
Explore all tools that specialize in sql-based stream processing. This domain focus ensures RisingWave delivers optimized results for this specific requirement.
Explore all tools that specialize in materialized view creation and maintenance. This domain focus ensures RisingWave delivers optimized results for this specific requirement.
Explore all tools that specialize in low-latency query serving. This domain focus ensures RisingWave delivers optimized results for this specific requirement.
Explore all tools that specialize in integration with apache iceberg. This domain focus ensures RisingWave delivers optimized results for this specific requirement.
Explore all tools that specialize in change data capture (cdc). This domain focus ensures RisingWave delivers optimized results for this specific requirement.
Stack materialized views on top of each other to build layered transformations, each incrementally updated, allowing for complex data pipelines with optimized performance.
Query past states of materialized views to reconstruct history for audits or backtesting, providing valuable insights into historical data.
Run multi-way joins across dozens of data streams and perform as-of joins between live and historical data, enabling advanced data enrichment and analysis.
Capture database changes with exactly-once semantics, including TOAST columns. Parallel, locality-aware backfill speeds up onboarding, ensuring data consistency.
Continuously ingest operational data from Postgres, Kafka, and other sources into Iceberg tables. Simplify ELT and ETL with automatic compaction, schema management, and efficient incremental ingestion.
Download the RisingWave binary or use a Docker image.
Start the RisingWave cluster using Docker Compose or Kubernetes.
Connect to the RisingWave cluster using a PostgreSQL client.
Create a source to ingest data from Kafka or other supported sources.
Define materialized views using SQL to transform and aggregate the data.
Query the materialized views to get real-time insights.
Integrate RisingWave with your application to serve real-time data.
All Set
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Verified feedback from other users.
"RisingWave is praised for its low-latency stream processing, SQL-based interface, and seamless integration with Apache Iceberg, making it suitable for real-time analytics and data lakehouse applications. Its cloud-native architecture and efficient resource utilization are also well-regarded."
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