# 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.

Repository: https://github.com/hazelcast/hazelcast
Canonical: https://ross.abutalabs.com/products/hazelcast
Homepage: https://www.hazelcast.com
Language: Java
License: NOASSERTION
License Family: other
Topics: java, hazelcast, in-memory, big-data, scalability, distributed, caching, hacktoberfest, stream-processing, low-latency, distributed-computing, distributed-systems, data-in-motion, data-insights, real-time
Last push: 2026-08-26T21:34:49+00:00
Link (homepage): https://www.hazelcast.com

## Health v2 (maintenance only)
Score: 82/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 51, longevity 100
- inputs: {"age_days": 5278, "days_push": 7, "days_rel": 112, "gap_med": 209, "n_releases_24m": 2}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6604, forks 1892 (observed 2026-08-28T04:09:45.662433+00:00)

## What it is
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
- artifact type: framework
- maturity: stable
- function: streaming, caching, message-queue, database, etl, machine-learning, search-engine
- domain: big-data, microservices, databases
- platform: jvm, cloud, cross-platform
- tags: 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

## Member repositories
- hazelcast/hazelcast (main) score 82
- hazelcast/hazelcast-jet (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:45.662433+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:43:31.374580+00:00, confidence not recorded.
  - readme: https://github.com/hazelcast/hazelcast (fetched 2026-08-28T04:09:45.662433+00:00, sha 4b075bfeb7bb)
  - homepage: https://www.hazelcast.com (fetched 2026-08-29T08:39:48.609225+00:00, sha 250fa629b780)
- Data as of 2026-08-30T08:39:29.467469+00:00.