Phase One, Analysis: Resolving What You Named

Catalyst's first move is unglamorous and unavoidable: it resolves your query against the catalog. When you wrote select category from products, the optimizer does not yet know that products is a real table, that category is a real column, or what type that column is. Analysis binds every name you used to a concrete thing in the catalog, the metadata store that knows which tables and columns exist and their types. This is where the errors you actually see come from. A misspelled column name, a table that does not exist, a type mismatch in a comparison: these are caught in analysis, because analysis is the moment Spark checks your named references against reality. The output of this phase is the resolved logical plan, a version of your query where every column and table is bound to its real

About This Interactive Section

This section is part of the Inside Catalyst: The Four Phases 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.