The best practices for incremental loading focus on efficiently capturing and processing only new or modified data while ensuring reliability and consistency. Here’s a structured approach:
1. Track Changes Reliably and Optimize Performance
Use mechanisms like timestamps (last_updated), incremental keys (e.g., auto-incrementing IDs), or database-specific features like Change Data Capture (CDC) to identify changes. CDC is particularly effective as it logs all inserts, updates, and deletions, avoiding gaps caused by backdated data. For performance, index columns used for tracking (e.g., last_updated) to speed up queries. Partition large tables by date or incremental keys to reduce scan times. For example, partitioning a sales table by order_date allows the database to skip irrelevant partitions during incremental fetches.
2. Handle Deletions and Ensure Data Consistency
Deletions are often overlooked in incremental loads. Use soft deletes (e.g., a deleted_at column) or leverage CDC to capture DELETE operations. Ensure transactional consistency by reading from a database snapshot or using isolation levels (e.g., READ COMMITTED) to avoid mid-load changes. For dependent data (e.g., dimension tables referenced by fact tables), process them in order to maintain referential integrity. For instance, load customer data before orders to ensure foreign keys exist.
3. Implement Checkpoints, Error Handling, and Monitoring
Store checkpoints (e.g., the last processed timestamp or ID) to resume failed loads without duplicates or gaps. Design idempotent processes—such as using MERGE statements—to handle retries safely. Log metrics like load duration, row counts, and errors for troubleshooting. Test edge cases: simulate partial updates, concurrent modifications, or schema changes (e.g., new columns) to ensure the pipeline adapts. For APIs without CDC, use webhooks or pagination with since parameters. Tools like Apache Spark can parallelize incremental loads for scalability.