9+ Streaming Databases Ranked & Compared

Compare streaming databases and event platforms ranked by GitHub stars, throughput, and real-time processing.

Last updated: May 20, 2026
8 databases
1Redis
Redis
74.4k+529 30d

In-memory data store used as a database, cache, message broker, and streaming engine

Key-Value·2009·RSALv2 / SSPLv1 / AGPLv3 (triple-licensed)·C
2Apache Flink
Apache Flink
26.0k+78 30d

Stateful stream processing framework for real-time and batch data at any scale

Streaming·2011·Apache-2.0·Java, Scala
3RisingWave
RisingWave
9.0k+88 30d

Postgres-compatible streaming database for real-time event processing and analytics

Streaming·2022·Apache-2.0·Rust
4Hazelcast
Hazelcast
6.6k−16 30d

Unified real-time data platform combining in-memory data grid with stream processing

Key-Value·2008·Apache-2.0·Java
5KurrentDB
KurrentDB
5.8k+22 30d

Event-native database for event sourcing and event-driven architectures with built-in streaming, formerly EventStoreDB

Streaming·2012·Kurrent License·C#
6YDB
YDB
4.7k+12 30d

Open-source distributed SQL database combining high availability, scalability, strong consistency, and ACID transactions

Relational·2022·Apache-2.0·C++
7NEventStore
NEventStore
1.6k0 30d

Persistence-agnostic event store library for .NET event sourcing and CQRS applications

Streaming·2010·MIT·C#
8HPE Ezmeral Data Fabric
HPE Ezmeral Data Fabric

Converged data platform with integrated NoSQL database, file system, and event streams for hybrid cloud

Multi-Model·2009·proprietary·C++, Java

What is a Streaming Database?

A streaming database processes data continuously as it arrives rather than storing it first and querying it later. Unlike traditional databases where you load data then run queries, streaming databases run persistent queries that produce results in real time as new events flow through. This enables use cases where timeliness matters — fraud detection, live dashboards, IoT alerting, and real-time feature computation. The category includes streaming platforms (Apache Kafka, Redpanda), stream processors (Apache Flink, RisingWave), and materialized view engines (Materialize) that maintain continuously updated query results over streaming data.

When to Use a Streaming Database

Use streaming databases when your application needs to react to data as it happens — real-time fraud detection, live analytics dashboards, IoT sensor alerting, event-driven microservices, and change data capture (CDC) pipelines. Apache Kafka and Redpanda serve as the event backbone, durably storing event streams. RisingWave and Materialize let you write SQL queries that maintain live results over those streams. Apache Flink handles complex event processing and windowed aggregations. Consider batch-oriented analytics databases (ClickHouse, DuckDB) when real-time isn't required and periodic processing is sufficient.

Frequently Asked Questions

What is the difference between a streaming database and a traditional database?
Traditional databases store data at rest and answer queries on demand — you insert data, then query it later. Streaming databases process data in motion — queries run continuously and produce updated results as new events arrive. A traditional database answers 'what is the current count of orders?' at the moment you ask. A streaming database maintains a live, always-updated count that changes the instant a new order appears.
Is Kafka a database?
Kafka is primarily a distributed event streaming platform — it durably stores ordered streams of events and lets consumers read them. It functions as a database in the sense that it persists data and supports replay, but it lacks query capabilities found in traditional databases (no SQL, no secondary indexes, no joins). Kafka is often paired with a stream processor like RisingWave, Flink, or Materialize that adds SQL query capabilities over Kafka streams.
What is the difference between Kafka and Redpanda?
Redpanda is a Kafka-compatible streaming platform rewritten in C++ without the JVM dependency. It offers lower latency, simpler operations (single binary, no ZooKeeper), and better resource efficiency. Existing Kafka clients and configurations work with Redpanda unchanged. Choose Redpanda for new deployments where operational simplicity and performance matter. Choose Kafka when you need the broader ecosystem, enterprise support from Confluent, or Kafka-specific features like Kafka Streams.
What is RisingWave?
RisingWave is an open-source streaming database that lets you write standard SQL queries over streaming data. It maintains materialized views that update in real time as new events arrive — similar to what Materialize offers. RisingWave is PostgreSQL wire-compatible, so existing Postgres drivers and tools work. It's designed for real-time analytics, monitoring dashboards, and event-driven applications where you want SQL simplicity over Kafka's complexity.
When should I use stream processing vs batch processing?
Use stream processing when timeliness matters — fraud detection (must react in seconds), live dashboards (need real-time metrics), IoT alerting (sensors report anomalies immediately), and event-driven architectures. Use batch processing when completeness and cost matter more than speed — daily reports, ML model training, data warehouse ETL, and historical analysis. Many systems use both: stream processing for real-time signals and batch processing for comprehensive analysis. This is known as the Lambda architecture.

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