What is Data Lakehouse
Data Lakehouse - Hybrid data architecture
A hybrid architecture combining features of data lakes (for raw multimodal data) and data warehouses (for structured querying).
How It Works
Data lakehouses combine the flexibility of data lakes for storing raw data with the structured querying capabilities of data warehouses. This enables organizations to manage both structured and unstructured data in a single platform.
Technical Details
Implements table formats like Delta Lake, Iceberg, or Hudi to provide ACID transactions, schema enforcement, and versioning over raw data files. Uses metadata layers to manage schema and optimize query performance.
Best Practices
- Implement clear data organization strategies
- Use appropriate file formats for different data types
- Maintain data quality through schema validation
- Optimize storage tiers for different access patterns
- Implement proper data governance and security
Common Pitfalls
- Not properly planning data organization
- Overlooking data quality and validation
- Inefficient storage tier management
- Poor governance and security implementation
- Inadequate metadata management
Advanced Tips
- Use table formats for transactional consistency
- Implement data clustering for query optimization
- Leverage caching for frequently accessed data
- Set up automated data quality monitoring
- Use columnar formats for analytical workloads