When building data pipelines, choosing names and patterns that respect existing subsystems prevents confusion and deployment friction. This article outlines practical guardrails, real-world anti-patterns, and scalable ET strategies.
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.