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Datasets:
datamatastudios
/
data-tool-momentum

Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
< 1K
Tags:
open-source
developer-tools
data-engineering
github
software-trends
data-tools
Libraries:
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pandas
Polars
License:
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metadata 
license: cc-by-4.0
pretty_name: Datamata Data Tool Momentum Index
language:
  - en
tags:
  - open-source
  - developer-tools
  - data-engineering
  - github
  - software-trends
  - data-tools
size_categories:
  - n<1K
source_datasets:
  - original
configs:
  - config_name: default
    data_files: data-tool-momentum.csv

Datamata Data Tool Momentum Index

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.

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." 2026-07-19. https://www.datamatastudios.com/datasets/data-tool-momentum. Licensed under CC BY 4.0.