A comprehensive web-based assessment tool designed to evaluate your organization's technical maturity across five critical Databricks pillars and provide actionable recommendations for improvement.
The Databricks Technical Maturity Assessment Framework is a sophisticated tool that helps organizations:
- Evaluate their current technical maturity across 5 key pillars
- Identify gaps between current and desired states
- Receive tailored recommendations based on latest Databricks tools and features
- Plan their journey from current to desired maturity levels
Each pillar contains 25 questions organized into 5 logical dimensions:
- Governance & Compliance - Data governance, security, privacy, lineage, quality
- Scalability & Performance - Resource optimization, availability, monitoring, global deployment
- Integration & Connectivity - Enterprise systems, APIs, streaming, cloud/hybrid, data movement
- Automation & DevOps - Infrastructure automation, CI/CD, configuration, backup, monitoring
- Innovation & Future Readiness - Feature adoption, emerging technologies, experimentation, partnerships
- Data Ingestion & Sources - Multi-source ingestion, streaming/batch, quality validation, scale, lineage
- Data Transformation & Processing - ETL/ELT, cleansing, business logic, aggregation, optimization
- Data Architecture & Analytics - Scalability, modeling, query performance, BI integration, visualization
- Data Quality & Governance - Quality checks, profiling, governance, security, privacy, retention
- Operations & Performance - Pipeline orchestration, monitoring, backup/recovery, capacity planning, optimization
- Model Development & Training - Development, feature engineering, training, validation, versioning
- Model Deployment & Serving - Deployment, serving, A/B testing, rollback, scaling
- Model Monitoring & Maintenance - Performance monitoring, drift detection, retraining, explainability, governance
- ML Lifecycle Management - Lifecycle management, orchestration, workflow, resource management, data management
- ML Operations & Infrastructure - Infrastructure, security, backup, optimization, change management
- AI Foundation & Infrastructure - AI infrastructure, model management, security, data management, monitoring
- AI Development & Training - Model development, prompt engineering, training, validation, experiment tracking
- AI Deployment & Serving - Deployment, serving, APIs, scaling, rollback
- AI Applications & Use Cases - RAG, agents, conversational AI, content generation, customization
- AI Governance & Ethics - Governance, ethics, explainability, risk management, change management
Each question is assessed using a 5-level maturity scale:
- Initial - Ad-hoc processes, limited documentation
- Developing - Some processes defined, basic tools in use
- Defined - Processes documented, tools standardized
- Managed - Processes measured, continuous improvement
- Optimized - Best practices, innovation-driven
- Interactive Forms - User-friendly assessment interface for each pillar
- Progress Tracking - Real-time progress indicators and completion status
- Pain Point Identification - Multi-select options for technical and business pain points
- Save Progress - Ability to save and resume assessments
- Responsive Design - Works on desktop, tablet, and mobile devices
- Gap Analysis - Automatic identification of gaps between current and desired states
- Priority Scoring - High/Medium/Low priority recommendations based on gap size
- Tool Recommendations - Latest Databricks tools and features for each gap
- Benefit Analysis - Key benefits and value propositions for each recommendation
- Export Capabilities - Download recommendations as JSON reports
The framework includes recommendations for the latest Databricks tools and features:
- Unity Catalog - Centralized governance for data and AI assets
- Serverless Compute - Auto-scaling infrastructure for optimal resource utilization
- Lakehouse Architecture - Unified platform combining data lakes and data warehouses
- Delta Lake - Open-source storage layer with ACID transactions
- Delta Live Tables - Declarative ETL framework for reliable data pipelines
- Lakeflow Designer - Low-code/no-code environment for building ETL pipelines
- Databricks SQL - High-performance SQL analytics on data lakes
- Photon Engine - Optimized query execution engine
- SQL Warehouse - Managed SQL compute for analytics workloads
- Apache Spark - Unified analytics engine for large-scale data processing
- BI Integration - Seamless integration with business intelligence tools
- MLflow - End-to-end ML lifecycle management
- AutoML - Automated model development and hyperparameter tuning
- Feature Store - Centralized feature management
- Mosaic AI - Production-ready AI agent framework
- Vector Search - Vector search and RAG capabilities
- Agent Framework - Enterprise AI governance and deployment
- Modern web browser (Chrome, Firefox, Safari, Edge)
- No additional software installation required
- Clone or download the assessment framework files
- Open
index.htmlin your web browser - Start the assessment by clicking "Start Assessment"
- Complete Assessments - Navigate through each of the 4 pillars and answer questions (all questions are optional)
- Select Maturity Levels - Choose current and/or desired maturity states for each question
- Identify Pain Points - Select relevant technical and business pain points
- Save Progress - Use the save feature to preserve your work
- Generate Recommendations - View tailored recommendations based on your responses (partial completion allowed)
- Export Results - Download your assessment results and recommendations
- Review the framework structure and pillars
- Understand the assessment process
- Start the assessment
- Complete each of the 4 pillar assessments (25 questions per pillar - all optional)
- Select current and/or desired maturity levels
- Identify technical and business pain points
- Save progress as needed
- Generate recommendations at any time (partial completion allowed)
- Review gap analysis and priority recommendations
- Explore recommended Databricks tools and features
- Understand key benefits and value propositions
- Export recommendations for implementation planning
The framework is designed to be easily customizable:
- Questions - Modify or add questions in the assessment data files
- Maturity Levels - Adjust maturity level definitions and descriptions
- Tools - Update Databricks tools and recommendations
- Styling - Customize the visual design and branding
- Logic - Modify recommendation algorithms and scoring
databricks-assessment/
├── index.html # Main application interface
├── styles.css # Custom styling and responsive design
├── app.js # Main application logic and functionality
├── assessment-data.js # Core assessment data (Platform, Data)
├── assessment-data-extended.js # Extended assessment data (ML, GenAI)
└── README.md # This documentation file
- HTML5 - Semantic markup and structure
- CSS3 - Modern styling with Bootstrap 5 integration
- JavaScript (ES6+) - Interactive functionality and assessment logic
- Bootstrap 5 - Responsive UI framework
- Font Awesome - Icons and visual elements
- Chrome 90+
- Firefox 88+
- Safari 14+
- Edge 90+
- Local Storage - Assessment progress and responses
- JSON Export - Recommendations and reports
To contribute to the assessment framework:
- Fork the repository
- Create a feature branch
- Make your changes
- Test thoroughly
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
For support and questions:
- Review the documentation
- Check the code comments
- Create an issue in the repository
The framework is regularly updated to include:
- Latest Databricks tools and features
- New assessment questions and dimensions
- Enhanced recommendation algorithms
- Improved user experience features
Built with ❤️ for the Databricks community