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datamatastudios/data-tool-momentum

Datamata Data Tool Momentum Index Cross-signal momentum for open source data tools: GitHub stars, forks and 4-week star growth, PyPI and npm downloads, and active job demand. One row per tool from the most recent weekly snapshot, with a 0-100 momentum score. Latest snapshot: 2026-10-04 Tools in this release: 26 Updated: weekly Licence: CC BY 4.0 — free to use and adapt, including commercially, with attribution. Source & methodology:… See the full description on the dataset page: https://huggingface.co/datasets/datamatastudios/data-tool-momentum.

sourceHugging Facecc-by-4.0updated 7d agoView on Hugging Face
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Datamata Data Tool Momentum Index

Cross-signal momentum for open source data tools: GitHub stars, forks and 4-week star growth, PyPI and npm downloads, and active job demand. One row per tool from the most recent weekly snapshot, with a 0-100 momentum score.

  • —Latest snapshot: 2026-10-04
  • —Tools in this release: 26
  • —Updated: weekly
  • —Licence: CC BY 4.0 — free to use and adapt, including commercially, with attribution.
  • —Source & methodology: <https://www.datamatastudios.com/datasets/data-tool-momentum>

Quickstart

python
import pandas as pd

# Stream straight from the Hub — no download step needed
df = pd.read_csv("hf://datasets/datamatastudios/data-tool-momentum/data-tool-momentum.csv")

# Tools with the most momentum right now
print(df.sort_values("momentum_score", ascending=False).head(10))

Or load it with the 🤗 datasets library:

python
from datasets import load_dataset

ds = load_dataset("datamatastudios/data-tool-momentum")

What you can answer with it

  • —Which open source data tools have the most momentum, blending GitHub, downloads and job demand.
  • —Which tools are gaining GitHub stars fastest over the trailing four weeks (star_growth_4w_pct).
  • —How ecosystem adoption (pypi_downloads_month, npm_downloads_month) lines up with real hiring demand (job_listing_count).
  • —How any signal moves over time, by appending each weekly snapshot.

Columns

ColumnTypeDescription
snapshot_datestringUTC date the latest snapshot was taken (YYYY-MM-DD).
toolstringTool name (e.g. dbt, Apache Airflow, DuckDB).
slugstringStable identifier used across Datamata surfaces.
categorystringTooling category: transform, orchestrator, processing, streaming, ingestion, bi, ml, ai, mlops, warehouse or quality.
starsnumberGitHub stargazers on the snapshot date.
forksnumberGitHub forks on the snapshot date.
open_issuesnumberOpen GitHub issues on the snapshot date.
pypi_downloads_monthnumberPyPI downloads in the trailing month. Blank for tools not on PyPI.
npm_downloads_monthnumbernpm downloads in the trailing month. Blank for tools not on npm.
job_listing_countnumberActive job listings mentioning the tool. Blank for tools not in the skill taxonomy.
star_growth_4w_pctnumberChange in GitHub stars over the trailing 4 weeks, as a percentage. Blank until 4 weeks of history exist.
momentum_scorenumber0-100 percentile composite of stars, job demand, downloads and 4-week star growth.
githubstringGitHub repository (owner/repo). Blank if not tracked on GitHub.
websitestringProject homepage.

How it is built

Each week we snapshot every tool from the GitHub REST API (stars, forks, open issues), pypistats.org and the npm registry (trailing-month downloads) and our active job listings. The momentum score is a percentile composite: 35% job demand, 30% GitHub stars, 20% downloads and 15% four-week star growth. Full method and known limitations: <https://www.datamatastudios.com/methodology>.

Citation

Datamata Studios. "Datamata Data Tool Momentum Index." 2026-10-04. https://www.datamatastudios.com/datasets/data-tool-momentum. Licensed under CC BY 4.0.