gitpowerdash 0.1.8

Git analytics specifically for Power BI projects

GitPowerDash 🚀

Empowering Executive Oversight through Git Analytics and Graph-based Star Schemas.

gitpowerdash is a lightweight Python CLI and library designed to bridge the gap between technical development history and executive-level project management. It transforms raw Git logs into a structured Star Schema, available as local CSVs or hydrated directly into a Microsoft Fabric Graph.

🚀 The Value Proposition

Traditional Git viewers are built for developers. gitpowerdash is built for Decision Makers.

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📊 The Star Paradigm

Whether you use CSVs or the Graph, the data is modeled for analytical performance:

• Dimensions (Nodes): Author and File entities.

• Facts (Edges): Commits that link Authors to Files, containing quantitative measures like Insertions and Deletions.

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🛠️ Installation

Bash

git clone https://github.com/youruser/gitpowerdash
cd gitpowerdash
uv sync

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📖 Usage

1. Direct CSV Export (Local Power BI)

The fastest way to get started. Generates a "Fact" and "Dimension" table structure in the ./export folder.

Bash

uv run gitpowerdash extract --output ./export

2. Fabric Graph Hydration (Cloud Intelligence)

To move beyond flat files and perform complex path-analysis in Fabric:

A. Configure Identifiers (Stored securely in OS Keyring):

Bash

uv run gitpowerdash configure

B. Login (Device Code Flow):

Bash

uv run gitpowerdash login

C. Hydrate (Transform and Push):

Bash

uv run gitpowerdash hydrate --workspace-id <fabric-workspace-uuid>

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🚀 Why Use the Graph?

While CSVs are great for basic velocity, Hydrating the Fabric Graph allows you to:

• Identify Knowledge Silos: Query paths to find files touched by only one author.

• Analyze Blast Radius: See which contributors are most "connected" to a failing module.

• Native Power BI Integration: Fabric Graph items are automatically available as Semantic Models.

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🔒 Security

gitpowerdash follows professional security standards:

• No Hardcoding: All Azure/Fabric IDs are user-configured.

• Keyring Storage: MSAL Token Caches and secrets are stored in your OS-native secure vault.

• Typing: Full Python type-hinting for robust integration.