# TIBCOSoftware/snappydata

Project SnappyData - memory optimized analytics database, based on Apache Spark™ and Apache Geode™. Stream, Transact, Analyze, Predict in one cluster

Repository: https://github.com/TIBCOSoftware/snappydata
Canonical: https://ross.abutalabs.com/products/snappydata
Homepage: http://www.snappydata.io
Language: Scala
License: NOASSERTION
License Family: other
Topics: snappydata, spark, memory-database, analytics, stream, transaction, scale
Last push: 2022-11-21T12:17:56+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4004, "days_push": 1381, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- 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 1033, forks 197 (observed 2026-08-28T04:03:18.466313+00:00)

## What it is
SnappyData (TIBCO ComputeDB) is a distributed, memory-optimized analytics database that fuses an in-memory hybrid OLTP/OLAP store inside Apache Spark, built on Apache Geode. It unifies stream processing, transactions, interactive SQL analytics, and machine learning in a single cluster.

## Use cases
- run interactive sub-second SQL analytics over large datasets without pre-aggregation
- combine streaming ingestion, transactions, and analytics in one cluster
- query external data sources like S3, Hadoop, and SQL databases via Spark connectors
- cache hot datasets in distributed memory for high-concurrency dashboards
- build real-time operational analytics applications
- run machine learning predictions alongside live transactional data

## When to choose
- you need low-latency, high-concurrency analytics fused with Spark and already run legacy SnappyData/TIBCO ComputeDB workloads
- you want a single cluster for streaming, transactions, and interactive SQL over big data sources

## When to avoid
- starting a new project - the repo is legacy, unmaintained, and may contain unpatched security vulnerabilities
- you need guaranteed security updates or vendor support
- you only need a standard data warehouse or modern lakehouse engine

## Facets
- artifact type: service
- maturity: abandoned
- function: database, caching, streaming, analytics, machine-learning
- domain: databases, big-data, analytics, microservices
- platform: jvm, windows, cloud
- tags: in-memory-database, apache-spark, apache-geode, sql, distributed-cluster, stream-processing, olap, legacy, scala, data-engineering, linux, macos, docker

## Member repositories
- TIBCOSoftware/snappydata (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:18.466313+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 2026-08-30T07:06:38.644892+00:00, confidence not recorded.
  - readme: https://github.com/TIBCOSoftware/snappydata (fetched 2026-08-28T04:03:18.466313+00:00, sha eed010d7c12f)
- Data as of 2026-08-30T08:39:29.467469+00:00.