Pull Ingestion from a Database

Concepts covered: paPullIngestion, paFullVsIncremental

Pull ingestion is the workhorse of analytical data engineering. A scheduled job opens a JDBC or SQLAlchemy connection to an operational database, runs a SELECT, writes the results to a raw landing zone, and exits. The pattern is decades old, well understood, and still the right answer for a large fraction of ingestion problems. The mechanics are simple. The trap is that simple mechanics tempt engineers to ignore the questions that decide whether the simple mechanics are correct. The Two Flavors: Full and Incremental The choice between full and incremental is the first design decision in pull ingestion. A 10000 row reference table is easy: read it whole every time, replace the destination, move on. A 10 billion row events table is impossible to read whole every run. Somewhere between those

About This Interactive Section

This section is part of the Ingestion Patterns: 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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DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.