apache/spark
Rank not captured·Top 100·All-time stars·Cached ranking snapshot·
Apache Spark - A unified analytics engine for large-scale data processing
First read
Visible and active, but adoption proof is still thinner
apache/spark has enough public attention and recent movement to stay on the shortlist, but package usage is still partial, so the next step should be source and ecosystem validation rather than a quick yes.
Visible
44K public stars in the current GitStar snapshot.
Recognizable in the ecosystem
Active
Last commit Sep 12, 2026.
Fresh activity
Adopted
Treat stars as discovery context until a linked package appears.
No linked package mapping
Confidence
One or more key signals are partial, so GitStar keeps the interpretation conservative.
Partial snapshot
Snapshot facts
- 44K stars
- 29.4K forks
- Last commit Sep 12, 2026
- Package usage not mapped yet
Compare lens
dbeaver/dbeaver and donnemartin/data-science-ipython-notebooks are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.
Signal trail
Trajectory
Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.
Daily momentum
Recent momentum is not captured
Weekly momentum
Recent momentum is not captured
Monthly momentum
Recent momentum is not captured
Last commit
Sep 12, 2026
Package reality
No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.
No linked package signal is expected for this project type, so the read leans more heavily on repository-level public signals.
Hype vs Reality
Insufficient Package Data
Package mapping
No linked package yet
Cross-links
Standalone repo read
Validation note
GitStar can summarize public signals for apache/spark, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.
🦕Living Fossil🏭Factory Floor
🛡️ Editorial Context
GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.
Why this rank
This repository stands out because it combines fresh update and stable visibility.
Fresh updateStable visibility
📈 Momentum & Adoption Signals
Approximate star trajectory
Reconstructed from current stars and cached daily/weekly/monthly deltas.
Now: 44K
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix (log-scaled for readability)
Current stars44K
🧭 Hype vs Reality
Insufficient Package DataGitStar can see repository momentum, but it does not have a reliable linked package signal yet.
Treat stars and recent movement as discovery context only until npm or PyPI usage is available.
Status
Insufficient Package Data
Package footprint
No linked npm or PyPI usage signal
Method note
This label appears when GitStar cannot find strong enough package telemetry to compare attention against adoption. Compare it against other Scala repos before treating stars as a moat.
Related alternative
dbeaver/dbeaver
Shares the data category footprint with apache/spark, so the comparison is closer to a same-problem decision than a same-language coincidence.
Related alternative
donnemartin/data-science-ipython-notebooks
Shares the data category footprint with apache/spark, so the comparison is closer to a same-problem decision than a same-language coincidence.
Related alternative
forthespada/CS-Books
Shares the data category footprint with apache/spark, so the comparison is closer to a same-problem decision than a same-language coincidence.
Related alternative
microsoft/ML-For-Beginners
Shares the data category footprint with apache/spark, so the comparison is closer to a same-problem decision than a same-language coincidence.
Research links
Cross-links
Compare against related repos
GitStar picked dbeaver + data-science-ipython-notebooks as the closest next comparison from the related repository set.
Repo utility
Embed Badge
Preview:
Markdown
[](https://gitstar.space/repo/apache/spark)HTML
<a href="https://gitstar.space/repo/apache/spark"><img src="https://gitstar.space/api/badge/apache/spark" alt="GitStar"></a>Styles:
Wider nearby ecosystem
Java
51.7K starsdbeaver/dbeaver
Free universal database tool and SQL client
Python
29.3K starsdonnemartin/data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Unknown
27.3K starsforthespada/CS-Books
🔥🔥超过1000本的计算机经典书籍、个人笔记资料以及本人在各平台发表文章中所涉及的资源等。书籍资源包括C/C++、Java、Python、Go语言、数据结构与算法、操作系统、后端架构、计算机系统知识、数据库、计算机网络、设计模式、前端、汇编以及校招社招各种面经~
Jupyter Notebook
90.3K starsmicrosoft/ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
C++
49.8K starsClickHouse/ClickHouse
ClickHouse® is a real-time analytics database management system
Java
28.1K starsOtterMind/Chat2DB
Chat2DB is a free, cross-platform, local-first database client and SQL workspace for developers, DBAs, analysts, and data teams. Connect to 40+ databases, manage data, edit and run SQL, and use your own AI model to generate, explain, and optimize queries. Available on desktop, web, Docker, and CLI, with MCP support.
Next step after the validation read
Move into a compare preset, organization view, or the heuristic notes once this first fold tells you whether the repo looks visible, active, adopted, and credible enough to keep researching.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
About This Page
This page provides a quick overview of apache/spark based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.
Want to show your project's ranking? Copy the badge embed code above and add it to your README.