Delta or Iceberg?

Concepts covered: paTableFormats

Both Delta Lake and Apache Iceberg add ACID transactions to files sitting on object storage. They solve the same core problem: Parquet files are immutable, so updates, deletes, and schema changes require a metadata layer. The interviewer wants you to know what each does well and where they diverge. The Core Problem They Solve Without a table format, a "table" is just a directory of Parquet files with a naming convention. There's no atomic commit - if a write fails halfway, you have partial data. There's no isolation - a reader might see a half-written batch. There's no schema enforcement - a new file could have different column names. Delta and Iceberg both fix this by adding a transaction log that tracks which files constitute the current table state. Time Travel Both formats suppor

About This Interactive Section

This section is part of the The Storage Question: 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 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.