Build vs Buy at Each Layer

Concepts covered: paBuildVsBuy

Every layer of a pipeline can be built in-house or bought from a vendor. The choice is rarely all build or all buy; the right answer differs per layer. Ingestion has mature SaaS options (Fivetran, Airbyte) that solve the boring 80% of source extraction at a real per-row cost. Orchestration has open-source options (Airflow, Dagster, Prefect) that have absorbed most of what custom schedulers used to do. Storage and warehousing have been almost entirely commoditized into Snowflake, BigQuery, Databricks, and a few others. Transform tooling has converged on dbt for SQL-shaped work and Spark for non-SQL. The build-versus-buy conversation has shifted from 'should we build a warehouse' (no) to 'should we build the connector for this one weird source' (sometimes). The Calculus Layer-by-Layer The Hi

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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