Overview
This document outlines a high-level architecture for implementing an enterprise knowledge management system using Databricks for data processing and Kobai Semantic Model for knowledge representation. You can learn more about Kobai Platform here: https://www.kobai.io/
Core Components
1. Databricks Platform
- Delta Lake Storage: Provides reliable data storage with ACID properties
- Spark Processing: Handles large-scale data processing
- ML Pipeline: Supports machine learning model training and inference
2. Kobai Semantic Layer
- Knowledge Graph: Represents relationships between entities
- Semantic Model: Defines the business ontology
- Inference Engine: Generates new insights from existing data
3. Integration Points
- Data Ingestion: Multiple source connectivity
- Processing Pipeline: Real-time and batch processing
- API Layer: Standardized access patterns
Use Case: Product Development Intelligence
Business Context
A manufacturing company needs to connect product development data across:
- Research & Development
- Supply Chain
- Customer Feedback
- Market Analysis
- Regulatory Compliance
Implementation Strategy
- Data Collection Phase
- Ingest data from various sources into Databricks
- Apply quality checks and transformations
- Store in Delta Lake format
- Knowledge Processing
- Transform structured data into knowledge graph entities
- Apply semantic models to standardize terminology
- Generate relationships between entities
- Intelligence Layer
- Apply inference rules to discover patterns
- Generate recommendations
- Identify potential issues or opportunities
- Application Integration
- Expose REST APIs for applications
- Provide GraphQL endpoints for flexible queries
- Support real-time notifications
High Level Architecture
Benefits
- Data Integration
- Single source of truth
- Consistent data quality
- Real-time updates
- Knowledge Discovery
- Automated relationship identification
- Pattern recognition
- Predictive insights
- Business Value
- Faster decision making
- Reduced redundancy
- Improved collaboration
Data Flow Process Diagram
Implementation Phases
- Foundation (Month 1-2)
- Set up Databricks environment
- Configure Delta Lake storage
- Establish basic data pipelines
- Knowledge Layer (Month 2-3)
- Deploy Kobai Semantic Model
- Define initial ontologies
- Create base semantic rules
- Integration (Month 3-4)
- Connect data sources
- Implement processing logic
- Build initial APIs
- Enhancement (Month 4-6)
- Add advanced features
- Optimize performance
- Expand use cases
Key Metrics
- Technical Metrics
- Data processing latency
- Query response time
- System availability
- Business Metrics
- Time to insight
- Decision accuracy
- Cost savings
- Operational Metrics
- Data quality scores
- Integration success rates
- API usage patterns
Success Criteria
- Short Term
- Successful data integration
- Working semantic model
- Basic API functionality
- Medium Term
- Automated insights generation
- Reduced manual data processing
- Improved decision accuracy
- Long Term
- Full enterprise adoption
- Measurable business impact
- Scalable architecture
Recommendations
- Start Small
- Begin with a focused use case
- Validate the approach
- Scale gradually
- Focus on Quality
- Ensure data accuracy
- Validate semantic models
- Test thoroughly
- Plan for Scale
- Design for growth
- Consider performance early
- Build modular components
