---
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](dataset-cover-image.png)

# 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.