Stream Processing Engines

RisingWave is a PostgreSQL-compatible streaming database that maintains materialized views incrementally rather than recomputing them, storing state and tables in object storage so compute and storage scale separately.

Materialize incrementally updates SQL results as data arrives rather than recomputing them, built on Timely and Differential Dataflow and reachable over the PostgreSQL wire protocol, with strict serializability by default.

Apache Beam is a unified programming model for batch and streaming pipelines, written once in Java, Python, or Go and executed by a runner — Flink, Spark, Dataflow, or others — rather than by an engine of its own.

Google Cloud Dataflow is a fully managed service that executes Apache Beam pipelines for batch and streaming, provisioning and autoscaling worker VMs automatically and processing every record exactly once by default.

Azure Stream Analytics is a fully managed stream processing engine using a SQL dialect extended with temporal constraints, running in the cloud or on IoT Edge with the same tools and query language.

Arroyo is a distributed stream processing engine written in Rust that defines pipelines in SQL, performing stateful computation over bounded and unbounded sources with event-time processing and watermarks.

Apache Spark is an open-source distributed compute engine for batch processing, SQL, machine learning, and stream processing across large clusters.

Apache Flink is an open-source framework for stateful computation over unbounded and bounded data streams, with event-time processing, exactly-once state consistency, and CDC ingestion via Flink CDC.