File Drop Ingestion

Concepts covered: paFileIngestion

File drop ingestion is the lowest-tech and most common shape in cross-company integrations. A partner system writes a file to a known location at a known cadence, and the pipeline picks it up. The location is usually an S3 prefix, a GCS bucket, an Azure Blob container, or an SFTP directory. The cadence is whatever the partner promised, which is often whatever the partner happens to do. Senior engineers respect file drops because they handle the messy real world, and they distrust file drops because the messy real world keeps showing up in production. The Mechanics of a File Drop The Manifest Pattern The single most important piece of state in file drop ingestion is the manifest: the record of which files have already been processed. Without it, the pipeline reprocesses every file every run

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.