gitpowerdash 0.1.1

Git analytics specifically for Power BI projects

GitPowerDash

Empowering Executive Oversight through Git Analytics for Power BI.

gitpowerdash is a lightweight Python CLI and library designed to bridge the gap between technical development history and executive-level project management. It extracts Git repository metadata into a structured format optimized for Power BI's star schema.

🚀 The Value Proposition

Traditional Git viewers are built for developers. gitpowerdash is built for Decision Makers. By integrating this data into Power BI, you can:

• Visualize Velocity: Correlate commit frequency with project milestones.
• Identify Volatility: Spot "hot" files or modules that change too frequently.
• Mitigate Knowledge Silos: See which team members are the sole contributors to critical components.
• Scale with Fabric: Use as a local CLI or import directly into Microsoft Fabric Notebooks.

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🛠️ Installation

gitpowerdash is built with uv and is available via PyPI.

pip install gitpowerdash
# OR using pipx for a global CLI
pipx install gitpowerdash

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📖 Usage

CLI (Local Development)

Navigate to any Git repository and run the extraction. By default, this will create an export/ directory containing the CSV files for your Power BI dashboard.

gitpowerdash extract --output ./export

Library (Fabric / Python)

You can also use the core logic directly in Python scripts or Fabric Notebooks to write data directly to a Lakehouse.

from gitpowerdash import get_commits, get_file_changes

# Returns a Pandas DataFrame
commits_df = get_commits()
changes_df = get_file_changes()

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📊 Data Model

The CLI generates two primary tables linked by the SHA (Commit ID).

Table	Purpose	Key Columns
commits_fact.csv	High-level "Who, When, What"	SHA, Author, Date, Subject
file_changes_fact.csv	Granular "Where and How Much"	SHA, FilePath, Insertion, Deletion

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📋 Recommended Power BI Setup

1. Load: Import both CSVs into Power BI Desktop.
2. Relate: Create a One-to-Many relationship from commits_fact[SHA] to file_changes_fact[SHA].
3. Analyze: Use a Treemap visual with FilePath for size (by Insertions) to identify the most active parts of your codebase.

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License

Distributed under the MIT License. See LICENSE for more information.

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✨ Contributors

• George Bennett - City of Memphis Wastewater