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Computer Science
Databases
Data Lakes and Lakehouses
1. Introduction to Modern Data Architectures
2. Traditional Data Warehouse Architecture
3. Data Lake Architecture and Concepts
4. Data Lakehouse: The Unified Architecture
5. Core Technologies Enabling Data Lakehouses
6. Implementation and Management Strategies
7. Use Cases and Application Patterns
8. Architecture Comparison and Selection
9. Future Trends and Emerging Patterns
Architecture Comparison and Selection
Data Warehouse vs. Data Lake Analysis
Structural Differences
Schema Requirements
Data Type Support
Processing Approaches
Performance Characteristics
Query Response Times
Throughput Capabilities
Scalability Patterns
Cost Analysis
Storage Costs
Compute Costs
Operational Expenses
Use Case Alignment
BI and Reporting
Advanced Analytics
Data Exploration
Data Lake vs. Data Lakehouse Evaluation
Management Capabilities
Data Governance
Quality Control
Security Features
Transaction Support
ACID Compliance
Concurrency Handling
Data Consistency
Analytical Performance
SQL Query Performance
BI Tool Integration
Real-Time Processing
Data Warehouse vs. Data Lakehouse Assessment
Flexibility Comparison
Schema Evolution
Data Type Support
Processing Flexibility
Advanced Analytics Support
Machine Learning Integration
Data Science Workflows
Unstructured Data Processing
Total Cost of Ownership
Infrastructure Costs
Operational Expenses
Licensing Considerations
Architecture Selection Framework
Requirements Assessment
Business Objectives
Technical Requirements
Organizational Constraints
Data Characteristics Analysis
Volume Considerations
Variety Assessment
Velocity Requirements
Workload Analysis
Analytical Needs
Operational Requirements
Future Growth Projections
Decision Matrix Development
Evaluation Criteria
Scoring Methodology
Risk Assessment
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7. Use Cases and Application Patterns
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9. Future Trends and Emerging Patterns