PATTERN LIBRARY
Battle-tested data engineering patterns. Learn from the community, fork and customize.
SCD TYPE 2 HANDLING PATTERN
Efficiently handle slowly changing dimensions with historical tracking in data warehouses. Includes support for effective dates, surrogate keys, and audit columns.
INCREMENTAL LOADING PATTERN
Optimize batch loads by processing only new/changed data with robust watermarking and checkpointing. Supports multiple incremental strategies.
CDC PIPELINE PATTERN
Capture and process change data in near real-time using Debezium and Kafka. Includes schema evolution handling and exactly-once semantics.
DATA QUALITY FRAMEWORK
Comprehensive data quality checks with automated anomaly detection, threshold-based alerting, and self-healing pipelines.
EVENT SOURCING PATTERN
Implement event-driven architecture with full audit trail, temporal queries, and CQRS support for analytics workloads.
MULTI-TENANT ETL PATTERN
Design patterns for building scalable multi-tenant data pipelines with tenant isolation, resource management, and configuration.