| --- |
| 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 |
|
|
| 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:** 2026-07-19 |
| - **Tools in this release:** 26 |
| - **Updated:** weekly |
| - **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/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 |
|
|
| | 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. |
|
|
