Boosting BigQuery Performance with Materialized Views

As data volumes continue to grow, businesses are constantly searching for ways to make analytics faster and more cost-efficient. One of the most effective tools in Google BigQuery for achieving this balance is the materialized view. By caching precomputed results, materialized views reduce redundant query execution and deliver faster insights with lower processing costs. This post will walk you through what materialized views are, how to use them, and what recent improvements make them even more powerful.

Understanding Materialized Views

A materialized view is essentially a stored query result. Unlike standard views, which calculate data fresh with each query, materialized views keep the results and update them incrementally when the underlying data changes. This approach significantly speeds up query performance and reduces compute costs, making it ideal for recurring workloads.

Why Use Materialized Views?

Materialized views shine in use cases such as:

• Dashboards that repeatedly run the same summary queries.
• Reporting pipelines with frequent filters and joins.
• Time-based aggregations on very large datasets.
• Incremental update processes that benefit from cached results.

The advantages include:

• Faster response times.
• Lower data scan costs.
• Automatic refreshes when source data updates.
• Easy integration with common BI tools.

How to Create and Query a Materialized View

1. Prepare your data: Suppose you have a transactions table in BigQuery containing time, amount, and customer details.
2. Create the materialized view: For example, to calculate daily revenue totals:

CREATE MATERIALIZED VIEW `project_id.dataset.daily_revenue_mv` AS
SELECT 
  DATE(transaction_time) AS transaction_date,
  SUM(amount) AS total_revenue
FROM `project_id.dataset.transactions`
GROUP BY transaction_date;

3. Query the view: Once created, you can query it just like a table, with results returning much faster.
4. Refresh behavior: BigQuery automatically updates materialized views when the base table changes. You can also trigger a manual refresh if needed.

Visualizing in Looker Studio

Materialized views integrate seamlessly with Looker Studio (formerly Data Studio). After connecting your BigQuery project, you can build dashboards that pull directly from the materialized view. Common visualizations include time-series charts for revenue, bar charts for customer activity, or scorecards for KPIs. Custom formatting, labels, and layouts make it easy to create polished dashboards for stakeholders.

Limitations to Keep in Mind

• Only SELECT queries are supported.
• Functions must be deterministic.
• Window functions and subqueries are not allowed.
• Each materialized view can reference only one base table.

What’s New in 2025

Google has expanded materialized view functionality with several key updates:

• Materialized view replicas: Distribute query loads across regions for improved global availability.
• Iceberg-compatible views: Integrate with Iceberg table formats for modern data lake architectures.
• INFORMATION_SCHEMA monitoring: Track refresh status, staleness, and last update times directly from metadata tables.
• Enhanced client library options: Programmatically fine-tune view definitions with parameters such as maxStaleness.
• Recommender system (preview): Automatically suggests materialized views based on your query patterns to reduce costs and improve performance.

Best Practices

To maximize the value of materialized views:

• Use them for predictable, aggregation-heavy queries.
• Pair them with partitioned or clustered base tables for even faster queries.
• Keep an eye on refresh costs and query performance over time.
• Leverage INFORMATION_SCHEMA for monitoring and auditing.
• Test queries with EXPLAIN or dry-run before deploying into production.

Conclusion

Materialized views are a simple yet powerful way to optimize BigQuery workloads. They cut query times, lower expenses, and streamline analytics pipelines, especially when paired with BI tools like Looker Studio. With the latest enhancements in 2025, materialized views are more flexible and insightful than ever, making them an essential part of a modern data strategy.