The Data Lake

Concepts covered: paDataLake, paParquet, paLakeZones

A data lake is files in object storage. That sentence sounds anticlimactic and is. The lake is not a database. It is a directory of files in S3, GCS, or Azure Data Lake Storage, organized by convention rather than enforced rules. Each file holds a chunk of data in some format (Parquet, ORC, JSON, CSV). Files are immutable once written. Reading is done by some external compute engine (Spark, Presto, Athena, Trino) that opens the files and parses them. The lake's superpower is cheap storage and complete schema flexibility; its weakness is that flexibility comes at the cost of consistency guarantees. What Lives in a Lake The folder structure is organized by date in nearly every production lake, because partition by ingestion date is the cheapest and most useful organizing principle. Files arr

About This Interactive Section

This section is part of the Storage Layers and Table Formats: 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.