Databases for Stream processing

Spark's SQL and DataFrame engine for large-scale batch, streaming and lakehouse processing.

Meta's embeddable LSM-tree key-value engine, the storage layer under dozens of databases.

A Java in-memory data grid and stream processor that embeds in your app or runs as a cluster.

In-memory, partitioned SQL engine running Java stored procedures serially for millisecond decisioning.

Embedded LSM storage engine that writes straight to object storage like S3.

An Apache-licensed, Postgres-compatible streaming database that stores state on S3 and writes Iceberg.

Industrial-IoT time-series database with a table-per-device model and built-in streaming and caching.

A single C++ binary for streaming SQL, built on ClickHouse's engine with streams and windows.

An incremental SQL engine grounded in DBSP theory that updates any SQL query from changes only.

A Postgres-compatible operational data warehouse that keeps SQL views incrementally up to date.

Rust-native, Spark Connect-compatible engine for SQL, DataFrame and streaming jobs without the JVM.

Streaming lakehouse table format with LSM-based upserts and changelogs, born in Apache Flink.

Confluent's SQL layer over Kafka Streams, still supported but superseded by Flink SQL for new work.

Distributed time-series database and scripting platform for quant finance and industrial analytics.

Columnar streaming storage for Flink with primary-key tables and tiering into the lakehouse.

A Rust stream processor with SQL pipelines, now owned by Cloudflare and powering Cloudflare Pipelines.

Converged C/C++ NoSQL platform mixing key-value, documents, time-series, stream processing, graph and ML.

An incremental view engine that keeps complex SQL results fresh inside your existing Postgres or MySQL.

Column-store time-series engine with the q language, a fixture of capital-markets tick data.

Distributed SQL mixing in-memory rowstore and columnstore for real-time analytics, search and vectors.

Open-source distributed SQL from Yandex combining row and column tables, topics and vector search.

In-memory, Cypher-compatible graph database in C++ for streaming and real-time graph workloads.

A purpose-built event store for event sourcing, with streams, subscriptions and projections.

MongoDB's own multi-cloud DBaaS, bundling search, vector search and stream processing.

The commercial, enterprise-hardened distribution of Apache Ignite for low-latency SQL and compute.

Apache parallel database for semi-structured JSON data with SQL++, LSM storage and spatial/text indexes.

Multi-model database for IoT and observability: time series, full-text, vector and key-value on Parquet.