When to Split, When to Merge

Concepts covered: paDagBoundaries, paAssetTriggers

Two pipeline architectures are equivalent in what they produce and very different in how they operate. One large DAG with sixty tasks runs as a single unit. Six DAGs with ten tasks each run as separate units. The choice is one of the most consequential architectural decisions a senior engineer makes, and it cannot be made once for all time; the right boundary changes as the system grows. The principle is simple to state and hard to apply: split when the cost of coupling exceeds the cost of coordination. Costs of a Single Large Pipeline Costs of Many Small Pipelines The Right Place to Split The boundary that ages well is the boundary of ownership. A pipeline that is owned by one team should be one DAG. A pipeline that crosses team boundaries should split at the team boundary, with a clear c

About This Interactive Section

This section is part of the What a Data Pipeline Is: Advanced lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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