First DAG: 3 Tasks, 1 Schedule

Concepts covered: paFirstDag, paChainedDependencies

Vocabulary becomes useful when applied. The example below builds a tiny but complete DAG end to end. A retail company wants a daily summary of orders by region. Three tasks chain together: extract orders from Postgres, clean and standardize the rows, aggregate to one row per region per day. The DAG runs once a day at 2am Pacific. Every concept from the previous sections shows up in working code. Step 1: Name the Tasks Step 2: Declare the Dependencies The dependency graph is a chain. Clean reads what extract produces, so clean depends on extract. Aggregate reads what clean produces, so aggregate depends on clean. Two edges, three nodes, no cycles. The DAG is the smallest non-trivial example: a straight line. Step 3: Write the Airflow Code Three operators, one chain, one schedule. The defaul

About This Interactive Section

This section is part of the Orchestration and Dependencies: Beginner 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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