When the Schema Is Inside the Data

Concepts covered: sqlJsonExtract

The mental model: semi-structured is opaque until you extract A JSON column holds a value the database does not interpret as relational. Until you extract a path, you cannot filter, group, or aggregate on the content. Extraction returns a typed scalar (a STRING, INT, BOOLEAN) that downstream SQL operates on normally. The extraction step is the bridge between the semi-structured world and the relational world. State this when designing: 'I'll extract the path I need into named columns in a CTE, then the rest of the query operates on the extracted columns.'

About This Interactive Section

This section is part of the Semi-Structured Data: 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 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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