When working with incrementally loaded raw tables, the key data modeling strategy should balance performance, storage efficiency, and ease of access. A good generic approach follows these steps:
1. Understand Data Ingestion and Change Patterns
• Identify how data is incrementally loaded (append-only, upserts, or late-arriving updates).
• Determine if timestamps, versioning, or other fields track changes.
2. Implement a Staging Layer
• Maintain a staging table to store incremental raw data before transformation.
• If source data includes updates, consider Change Data Capture (CDC) methods (e.g., merge strategies).
3. Design an Efficient Processing Strategy
• Use an INSERT-ONLY approach when possible to avoid expensive updates.
• For deduplication or upserts, use partitioning + merge strategies instead of full-table scans.
• Consider delta processing (handling only new/changed records).
4. Use a Layered Data Model
A multi-tier architecture is beneficial:
• Raw Layer (Bronze): Stores incremental raw data in its original format (no transformation).
• Processed Layer (Silver): Applies de-duplication, filtering, and joins to create a clean dataset.
• Aggregated Layer (Gold): Optimized for analytics and reporting (pre-aggregations, materialized views).
5. Optimize for Query Performance
• Use partitioning (by time, region, or business logic) for faster lookups.
• Use clustering or indexing on frequently queried columns.
• In cloud data warehouses (Snowflake, BigQuery, Redshift), leverage automatic clustering and pruning.
6. Handle Slowly Changing Dimensions (SCDs)
• For historical tracking, use Type 2 SCD (adding versioned records).
• For real-time updates, use soft deletes or effective dating.
7. Automate & Monitor the Pipeline
• Implement data quality checks (e.g., row counts, null checks).
• Use orchestration tools (Apache Airflow, dbt, Dataflow) to manage incremental loads.
• Track data lineage and auditing for governance.
8. Choose the Right Storage Format
• Use Parquet or Delta Lake for efficient storage and fast queries.
• Leverage streaming ingestion (Kafka, Kinesis) for real-time needs.
Conclusion
A layered approach with incremental processing, partitioning, and optimization ensures scalable and efficient data modeling. The key is to minimize unnecessary updates, efficiently manage history, and optimize for queries.