Ingestion Patterns

Concepts covered: paFileIngestion, paApiIngestion, paCdc, paBatchVsStreaming

Three ingestion patterns dominate: file drops, API pulls, and Change Data Capture. Each carries a different latency floor and a different failure shape. The choice between them is the first design decision, and the interviewer expects it justified by the source system's constraints rather than by familiarity with a tool. File Drops A source system exports files to cloud storage on a schedule, and the pipeline picks them up. CSV and JSON from legacy vendors, Parquet from modern ones. This is the default for any source you do not control: third-party data feeds, SaaS exports, partner integrations. Latency is measured in hours, which is acceptable when the requirement is a morning report or a daily training run. The risk worth naming unprompted is duplicate delivery. A vendor re-uploads yeste

About This Interactive Section

This section is part of the Design a Pipeline: Intermediate 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 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.

How DataDriven Lessons Work

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.