🔍 Databricks WAF Light Tooling

🚀 Overview

Databricks WAF Light Tooling is a lightweight, automated assessment tool built to evaluate Databricks Lakehouse implementations against the Well-Architected Framework (WAF) principles. It analyzes system tables, logs, and metadata to generate real-time scores and actionable recommendations that drive better governance, security, performance, and cost-efficiency.

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❗ Problem Statement

Building a secure, efficient, and well-governed Databricks Lakehouse requires continuous adherence to WAF principles. However, the assessments generally suffer from:

• ⏱️ Time-consuming processes
• 🔁 Inconsistencies in evaluation
• ⚙️ Lack of automation

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🌟 Opportunity Statement

A WAF Tool can solve these pain points by offering:

• ✅ Automated WAF assessments
• 📊 Real-time scoring
• 🛠 Actionable insights

…empowering customers to continuously optimize their Databricks environments with minimal effort.

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💡 Proposed Solution

Develop a lightweight WAF assessment tool that:

• Automates analysis using System Tables, audit logs, and workspace metadata
• Provides real-time scoring against WAF pillars
• Highlights gaps and improvement opportunities
• Offers low-friction deployment for both internal teams and customers

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🛠 Existing Alternatives

• Many teams build custom dashboards for monitoring.
• These are often:
  • ❌ Manually maintained
  • ❌ Inconsistent across customers
  • ❌ Hard to scale or reuse

Databricks WAF Light Tooling offers a reusable, scalable, and automated alternative.

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👥 End Users

1. Databricks Field Engineering

• Solution Architects, Customer Success Engineers, and Pre-sales teams
• Use the tool to assess customer environments and recommend WAF-aligned improvements

2. Databricks Customers

• Data Engineers, Platform Admins, and Architects
• Self-assess their environments and improve governance, security, and cost efficiency

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📦 Getting Started

⚙️ Install Steps for WAF AUTO Tooling

• Clone the GitHub Repository
  Get the WAF Automation Tooling code from the official GitHub repo.

• Run install.ipynb
  Execute the notebook to automatically deploy the "WAF ASSESSMENT" dashboard in your Databricks workspace.

  Note: This tool collects masked email addresses (first 5 characters only) and workspace IDs for usage analytics during the initial installation only. This is a one-time telemetry collection to understand adoption. To disable telemetry, simply set ENABLE_TELEMETRY = False on line 10 in the notebook or comment out that line.

• Open the Dashboard
  Access real-time insights, scores, and recommendations—all at your fingertips.

WAF-AUTO-FEIP-642.mp4

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🤝 Contributing

Want to make WAF assessments better? Contributions are welcome!
Please fork the repo, open an issue, or submit a pull request.

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📄 License

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

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📬 Contact

For feature requests, support, or feedback, please use GitHub Issues.

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