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: 2026-09-02
• 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

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:

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

Column	Type	Description
snapshot_date	string	UTC date the latest snapshot was taken (YYYY-MM-DD).
tool	string	Tool name (e.g. dbt, Apache Airflow, DuckDB).
slug	string	Stable identifier used across Datamata surfaces.
category	string	Tooling category: transform, orchestrator, processing, streaming, ingestion, bi, ml, ai, mlops, warehouse or quality.
stars	number	GitHub stargazers on the snapshot date.
forks	number	GitHub forks on the snapshot date.
open_issues	number	Open GitHub issues on the snapshot date.
pypi_downloads_month	number	PyPI downloads in the trailing month. Blank for tools not on PyPI.
npm_downloads_month	number	npm downloads in the trailing month. Blank for tools not on npm.
job_listing_count	number	Active job listings mentioning the tool. Blank for tools not in the skill taxonomy.
star_growth_4w_pct	number	Change in GitHub stars over the trailing 4 weeks, as a percentage. Blank until 4 weeks of history exist.
momentum_score	number	0-100 percentile composite of stars, job demand, downloads and 4-week star growth.
github	string	GitHub repository (owner/repo). Blank if not tracked on GitHub.
website	string	Project 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." 2026-09-02. https://www.datamatastudios.com/datasets/data-tool-momentum. Licensed under CC BY 4.0.