Hazelcast
Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights. observed · 2026-08-28
Health v2 · maintenance only
82/100
- Activity 99
- Release rhythm 51
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 209
- age_days: 5278
- days_rel: 112
- days_push: 7
- n_releases_24m: 2
Adoption not part of the score
6604 stars · 1892 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Hazelcast is a unified real-time data platform combining distributed stream processing with a fast, in-memory data store. It lets applications ingest, process, query, and act on streaming and batch data with low latency using SQL or a dataflow API.
Use cases
- process streaming data with stateful, fault-tolerant pipelines
- serve low-latency SQL queries over streaming and batch data
- cache contextual and transactional data with read/write-through patterns
- build pub-sub and queue-based messaging between microservices
- enrich event streams with historical data before storing
- deploy machine learning models into real-time data pipelines
- replicate data across regions or data centers
When to choose
- you need low-latency processing and querying of data in motion
- you want a unified platform combining stream processing and a distributed key-value store
- you need exactly-once or at-least-once processing guarantees
- you require distributed coordination, caching, and messaging for microservices
- you want connectors to Kafka, Hadoop, S3, RDBMS, and JMS in one platform
When to avoid
- you only need simple batch analytics without real-time requirements
- your workload is small enough for a single-node database or message broker
- you need a purely relational database with strong ACID transactions across complex schemas
- your team cannot operate a distributed JVM-based cluster
Facets
framework · maturity stable
streaming caching message-queue database etl machine-learning search-engine big-data microservices databases jvm cloud cross-platform in-memory-data-grid stream-processing distributed-computing low-latency data-in-motion sql pub-sub hazelcast-jet real-time data-engineering messaging docker kubernetes
2 sources
- readme: https://github.com/hazelcast/hazelcast · fetched 2026-08-28 · 4b075bfeb7bb
- homepage: https://www.hazelcast.com · fetched 2026-08-29 · 250fa629b780
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| hazelcast/hazelcast | main | 82 |
| hazelcast/hazelcast-jet | main | 10 |
For agents
markdown · JSON · MCP: product_card(name="hazelcast/hazelcast")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem