# apache/spark

Apache Spark - A unified analytics engine for large-scale data processing

Repository: https://github.com/apache/spark
Canonical: https://ross.abutalabs.com/products/spark
Homepage: https://spark.apache.org/
Language: Scala
License: Apache-2.0
License Family: permissive
Topics: python, scala, r, java, big-data, jdbc, sql, spark
Last push: 2026-08-27T00:33:15+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 4572, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 43882, forks 29348 (observed 2026-08-28T04:12:09.166108+00:00)

## What it is
Apache Spark is a unified analytics engine for large-scale data processing, providing high-level APIs in Scala, Java, Python, and R over an optimized distributed execution engine. It bundles higher-level tools including Spark SQL and DataFrames, a pandas API, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for real-time stream processing.

## Use cases
- process petabyte-scale datasets across a cluster
- run distributed SQL queries for analytics and reporting
- build ETL pipelines over batch and streaming data
- train machine learning models at scale with MLlib
- analyze large JSON, CSV, or Parquet files with DataFrames
- perform exploratory data analysis on big data
- process real-time event streams with Structured Streaming
- run graph analytics on large graphs with GraphX

## When to choose
- you need to process data too large for a single machine
- you want one engine for batch, streaming, SQL, and ML workloads
- you need a mature, battle-tested big data framework with a large ecosystem
- your team works in Python, Scala, Java, or SQL and wants pandas-like or SQL interfaces
- you need to query data in HDFS, S3, Hive, Cassandra, or JDBC databases

## When to avoid
- your datasets fit comfortably in memory on one machine - pandas or DuckDB are simpler
- you need low-latency per-event stream processing rather than micro-batch
- you want lightweight ad-hoc analytics without cluster infrastructure overhead
- you need a small-footprint embedded database rather than a distributed engine

## Facets
- artifact type: framework
- maturity: stable
- function: etl, streaming, machine-learning, data-science, search-engine
- domain: big-data, data-science, machine-learning, analytics, microservices
- platform: jvm, python, cross-platform, cloud, windows
- tags: spark, pyspark, dataframes, spark-sql, mllib, structured-streaming, graphx, distributed-computing, apache, scala, sql, data-engineering, docker, kubernetes, linux, macos

## Member repositories
- apache/spark (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:09.166108+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 2026-08-29T16:22:16.377639+00:00, confidence not recorded.
  - readme: https://github.com/apache/spark (fetched 2026-08-28T04:12:09.166108+00:00, sha 70f6be2e8ea1)
  - homepage: https://spark.apache.org/ (fetched 2026-08-29T07:46:06.703555+00:00, sha a291a9d9a452)
  - site_page: https://spark.apache.org/docs/latest (fetched 2026-08-29T07:46:06.712911+00:00, sha 07bd3c2857f4)
  - site_page: https://spark.apache.org/documentation.html (fetched 2026-08-29T07:46:06.715172+00:00, sha 731c16e1c2df)
  - site_page: https://spark.apache.org/docs/latest/quick-start.html (fetched 2026-08-29T07:46:06.719573+00:00, sha e471ff564584)
  - site_page: https://spark.apache.org/docs/latest/sql-performance-tuning.html (fetched 2026-08-29T07:46:06.722015+00:00, sha ad4596579581)
  - site_page: https://spark.apache.org/docs/latest/sql-ref-ansi-compliance.html (fetched 2026-08-29T07:46:06.724560+00:00, sha 647aec5b39fa)
  - site_page: https://spark.apache.org/docs/latest/sql-data-sources-json.html (fetched 2026-08-29T07:46:06.727458+00:00, sha dde447752a94)
  - site_page: https://spark.apache.org/faq.html (fetched 2026-08-29T07:46:06.717494+00:00, sha 707d496b9598)
- Data as of 2026-08-30T08:39:29.467469+00:00.