Apache Spark
Unified analytics engine
The score summarizes repository activity. Funding is unknown and excluded from the score. These repository signals do not establish financial stability, security, or maintainer availability. How to interpret this data.
Evidence and limitations
Activity source: apache/spark via the GitHub API. Counts describe this repository, which may be a mirror or only part of the project.
- Commits in 30 days
- 402
- Commits in 90 days
- 1416
- Authors accounting for half the sampled commits
- 7
The 200-commit author sample does not cover the full 90 days. Author counts are lower bounds; concentration describes the sample only.
Few commits can reflect mature software. Commit authors do not establish merge rights, available maintainers, organizational diversity, or knowledge distribution. Funding is unknown; donation links alone do not establish income or runway.
Correct this assessment or provide evidence →Ways to help Apache Spark
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Read the repository's contribution and security guidance before proposing work.
Overview
Apache Spark is a unified analytics engine for large-scale data processing with built-in modules for SQL, streaming, machine learning, and graph processing.
Importance
- Leading big data processing framework
- Powers data analytics worldwide
- Foundation for data engineering
- Critical for AI/ML pipelines
Key Features
- In-memory computing
- SQL support (Spark SQL)
- Streaming (Structured Streaming)
- MLlib for machine learning
Sustainability
Financial sustainability and maintainer capacity have not yet been reviewed against current primary sources. See the evidence and support sections above for available information and a way to suggest corrections.
Dependencies
