BigQuery Nested Fields vs Flat Tables: Design Guide

When designing data models in BigQuery, one of the most important architectural decisions you'll face is choosing between BigQuery nested fields vs flat tables. This choice fundamentally shapes how you store, query, and analyze your data, affecting everything from query performance to storage costs and development complexity.

The decision between nested structures using STRUCT and ARRAY types versus traditional flat, normalized tables represents a classic trade-off between denormalized convenience and relational precision. Understanding when and why to use each approach will make you a more effective data engineer and help you design systems that truly serve your business needs.

Understanding Nested Fields in BigQuery

BigQuery's nested fields allow you to store complex, hierarchical data structures directly within a single table using STRUCT and ARRAY data types. A STRUCT represents a record with named fields, similar to a JSON object, while an ARRAY contains multiple values of the same type.

Consider this example of a customer table with nested order data:

CREATE TABLE ecommerce.customers_nested (
  customer_id STRING,
  customer_name STRING,
  email STRING,
  orders ARRAY>
  >>
);

This nested structure captures the complete customer journey in a single table, preserving the natural hierarchy between customers, orders, and items. When you query this data, you can access nested fields using dot notation and special functions like UNNEST().

Strengths of Nested Fields

Nested fields excel in several scenarios. They eliminate the need for complex joins when analyzing related data, which can significantly improve query performance for analytical workloads. Storage efficiency often improves because you avoid duplicating parent-level information across multiple rows.

The nested approach also maintains data locality, keeping related information physically close on disk, and provides a more intuitive representation of naturally hierarchical data like user sessions, product catalogs, or organizational structures.

Drawbacks of Nested Fields

However, BigQuery nested fields vs flat tables comparison reveals some significant limitations of the nested approach. Query complexity increases substantially when you need to filter, aggregate, or join on nested elements. Consider this query to find customers who bought a specific product:

SELECT 
  customer_id,
  customer_name,
  order_data.order_id,
  item_data.product_name
FROM ecommerce.customers_nested,
UNNEST(orders) AS order_data,
UNNEST(order_data.items) AS item_data
WHERE item_data.product_id = 'PROD123';

This query requires multiple UNNEST operations and can become difficult to understand and maintain. Additionally, updates become problematic because BigQuery doesn't support direct updates to nested fields. To modify a single item within an order, you must rewrite the entire customer record.

Schema evolution also presents challenges. Adding new fields to nested structures requires careful consideration of existing data and may necessitate expensive table recreations.

The Flat Table Approach

Flat tables follow traditional relational database principles, normalizing data across multiple tables with clear relationships. The same ecommerce data would be split into separate tables:

CREATE TABLE ecommerce.customers (
  customer_id STRING,
  customer_name STRING,
  email STRING
);

CREATE TABLE ecommerce.orders (
  order_id STRING,
  customer_id STRING,
  order_date DATE,
  total_amount FLOAT64
);

CREATE TABLE ecommerce.order_items (
  order_id STRING,
  product_id STRING,
  product_name STRING,
  quantity INT64,
  price FLOAT64
);

This normalized structure makes individual queries simpler and more intuitive. Finding customers who bought a specific product becomes straightforward:

SELECT DISTINCT 
  c.customer_id,
  c.customer_name
FROM ecommerce.customers c
JOIN ecommerce.orders o ON c.customer_id = o.customer_id
JOIN ecommerce.order_items oi ON o.order_id = oi.order_id
WHERE oi.product_id = 'PROD123';

Flat tables support all SQL operations naturally, including updates, deletes, and complex aggregations. Schema changes are typically easier to implement, and the structure remains familiar to developers with traditional SQL experience.

Google Cloud Platform Perspective

BigQuery's architecture fundamentally changes how you should think about the nested fields vs flat tables decision compared to traditional databases. BigQuery's columnar storage format and massively parallel processing engine handle certain types of joins very efficiently, reducing one traditional advantage of denormalization.

However, BigQuery's pricing model based on data scanned gives nested fields a unique advantage. When analyzing related data, nested structures can significantly reduce the amount of data processed. A query analyzing customer order patterns might scan gigabytes less data with nested fields compared to joining multiple flat tables.

BigQuery's slot-based execution model also influences this decision. Queries with multiple large table joins consume more slots and may experience longer queue times during peak usage periods. Nested structures can reduce resource contention by eliminating complex joins.

The platform's integration with other Google Cloud services like Dataflow and Cloud Functions often favors nested structures for streaming data pipelines, where maintaining data relationships during real-time processing is crucial.

Detailed Example: E-commerce Analytics Platform

Let's examine a realistic scenario to understand when each approach makes sense. Consider an e-commerce company analyzing customer behavior with 10 million customers, 100 million orders, and 500 million order items.

For the nested approach, a typical analytics query might look like:

SELECT 
  customer_id,
  customer_name,
  ARRAY_LENGTH(orders) as total_orders,
  (
    SELECT SUM(order_data.total_amount) 
    FROM UNNEST(orders) AS order_data
  ) as lifetime_value
FROM ecommerce.customers_nested
WHERE 
  EXISTS(
    SELECT 1 FROM UNNEST(orders) AS order_data 
    WHERE order_data.order_date >= '2024-01-01'
  );

This query scans only the customers table, potentially processing significantly less data than equivalent joins across flat tables. However, the query complexity is higher and harder to optimize.

The flat table equivalent would join three tables but might scan more data due to repeated customer and order information across rows. The query would be more straightforward but potentially more expensive in terms of bytes processed.

In this scenario, the nested approach likely wins for analytical workloads focused on customer-centric analysis, while the flat approach would be better for product-centric analysis or operational queries requiring frequent updates.

BigQuery Nested Fields vs Flat Tables Decision Framework

Choose nested fields when you have naturally hierarchical data with stable relationships, primarily analytical workloads, infrequent schema changes, and cost optimization as a priority. Nested fields work best for immutable data like logs, events, or historical transactions where the hierarchical structure reflects real-world relationships.

Choose flat tables when you need frequent data updates, have complex many-to-many relationships, require maximum query flexibility, or work with teams more comfortable with traditional SQL. Flat tables are ideal for operational systems, data that changes frequently, or scenarios where different teams need to access subsets of the data independently.

Factor	Nested Fields	Flat Tables
Query Performance	Excellent for hierarchical analysis	Better for flexible querying
Storage Cost	Lower (less duplication)	Higher (normalization overhead)
Query Cost	Often lower (less data scanned)	Potentially higher (joins required)
Data Updates	Complex (full record rewrite)	Simple and efficient
Schema Evolution	Challenging	Straightforward
Query Complexity	High for nested operations	Familiar SQL patterns

Relevance to Google Cloud Certification Exams

This topic appears frequently in the Professional Data Engineer and Professional Cloud Architect certifications. Exam questions often present scenarios where you must choose between nested and flat approaches based on specific requirements.

A typical exam scenario might describe a gaming company storing player session data with events, achievements, and purchases. The question would ask about the optimal BigQuery table design. Look for clues about query patterns, update frequency, and cost optimization priorities.

If the scenario emphasizes real-time analytics on complete player sessions with minimal updates, nested fields would be correct. If it mentions frequent updates to individual game events or the need for flexible cross-player analysis, flat tables would be the better choice.

The Professional Machine Learning Engineer certification might present this choice in the context of feature engineering, where nested structures can simplify the creation of aggregate features for ML models.

Remember that exam questions often include cost optimization as a key factor. When cost is mentioned prominently, nested fields typically provide the advantage due to reduced data scanning and storage efficiency.

Hybrid Approaches and Best Practices

In practice, many successful BigQuery implementations use hybrid approaches. You might maintain a nested table for analytical workloads while also keeping flat tables for operational queries. This strategy leverages the strengths of both approaches while mitigating their respective weaknesses.

Consider using materialized views to provide flat representations of nested data or vice versa. This approach can give you query flexibility without maintaining duplicate storage.

When working with streaming data, consider your downstream requirements early. Dataflow pipelines can efficiently maintain both nested and flat representations simultaneously if your use cases demand it.

Migration Strategies

If you need to migrate between approaches, plan carefully. Moving from flat to nested structures typically requires complex ETL processes to reconstruct relationships. Moving from nested to flat structures involves UNNEST operations and careful handling of NULL values in optional nested fields.

Use BigQuery's data transfer service and scheduled queries to maintain synchronized versions during transition periods, allowing you to validate the new structure before fully committing to the change.

Conclusion

The choice between BigQuery nested fields and flat tables reflects a fundamental trade-off between analytical convenience and operational flexibility. Nested fields shine when you have stable, hierarchical data and primarily analytical workloads focused on cost optimization. Flat tables excel when you need maximum flexibility, frequent updates, and familiar SQL patterns.

Neither approach is universally superior. The best data engineers understand both options deeply and choose based on specific requirements around query patterns, update frequency, cost constraints, and team capabilities. By mastering both approaches, you'll be equipped to design BigQuery solutions that truly serve your organization's needs while optimizing for the factors that matter most to your specific use case.

Remember that your initial choice doesn't have to be permanent. As requirements evolve, you can migrate between approaches or adopt hybrid strategies that leverage the strengths of both patterns. The key is making an informed decision based on a clear understanding of the trade-offs involved.