Nested Aggregation and Reconstruction

Concepts covered: sqlJsonExtract

The fundamental architectural decision for JSON pipelines: extract at query time (schema-on-read) or extract at ingest time (schema-on-write). Each is right for a different workload. Schema-on-read Schema-on-write At ingestion, extract the JSON into typed relational columns. Downstream queries reference the columns directly; no extraction syntax in consumer queries. New fields require a pipeline change (add a column, extract the new field). Schema changes are explicit: the pipeline either succeeds with the new column or fails. Right for stable schemas, audit-heavy reporting, and consumer-facing dashboards where read performance matters. The bronze/silver/gold pattern Most production platforms run a hybrid. Bronze layer keeps the JSON as a VARIANT column (schema-on-read; ingest is flexible)

About This Interactive Section

This section is part of the Semi-Structured Data: 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 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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