How To Create Nested Schemas in Python Using the Google BigQuery API

Nested schemas optimize data storage, but creating and updating fields with nested records can be challenging.

Schema Design in BigQuery

Nested schemas optimize data storage, but creating and updating fields with nested records can be challenging. In BigQuery, "nested and repeated" fields (using the STRUCT and ARRAY types) are the gold standard for performance. They allow you to maintain complex relationships, like a news article and its various multimedia assets, without the overhead of massive, expensive joins.

However, defining these programmatically via the BigQuery API’s SchemaField method can feel like a game of "bracket-matching" Tetris. One of the challenges I encountered when learning how to create nested schemas was the lack of resources available, in particular, resources utilizing the SchemaField method. Using the New York Times’ free API, I’ll demonstrate the "right way" to build these schemas manually, and then how to leverage AI to speed up your workflow.

Build Your Pipeline To A Data Engineering Career

You’ve reached the limit of the public preview. The full version of this post includes the implementation details: The code, the edge cases, and the "why" behind the architecture.

When you join PipelineToDE, you get:

• The DA → DE Pathway Course: A structured roadmap to bridge the gap between analysis and engineering.
• Weekly Senior Deep Dives: Fresh, tactical insights on Python, Cloud (GCP/AWS), and modern orchestration delivered every week.
• Production-Ready Blueprints: Access to 80+ protected stories and code repos from my time in the trenches as a Senior DE
• The DE Job Board (Coming Soon): Exclusive access to a curated board of high-agency Data Engineering roles.