Late data is inevitable in event-driven systems. This post shows practical patterns—watermarks, allowed lateness, and log-based reprocessing—to keep your ETL pipelines accurate and reliable.
A practical guide to implementing schema evolution in Apache Iceberg ETL pipelines using Avro compatibility guarantees, covering the mechanics, architecture patterns, and production pitfalls.
Learn how to design a scalable lakehouse ETL architecture with Apache Iceberg and orchestrate transformations using dbt Core, ensuring schema evolution and reliable data pipelines.
A guide to dbt incremental models with time‑travel semantics, enabling fault‑tolerant ETL pipelines and historical data reconstruction.
Apache NiFi enables automated, directed data flow between systems with built-in backpressure and schema management, making it a robust choice for modern ETL architectures.