Materialized views in BigQuery are a powerful feature that can significantly improve query performance and reduce costs by precomputing and storing the results of a query. This section will cover the following topics:

1. What are Materialized Views?
2. Creating Materialized Views
3. Querying Materialized Views
4. Maintaining Materialized Views
5. Best Practices and Use Cases

Materialized views are precomputed views that store the result of a query. Unlike standard views, which compute their results each time they are queried, materialized views store the results and can be refreshed periodically. This can lead to significant performance improvements, especially for complex queries.

• Performance Improvement: Queries on materialized views are faster because the data is precomputed.
• Cost Efficiency: Reduces the amount of data processed by queries, leading to lower costs.
• Simplified Querying: Simplifies complex queries by breaking them into smaller, more manageable parts.

To create a materialized view, you use the CREATE MATERIALIZED VIEW statement. Here is a basic example:

• CREATE MATERIALIZED VIEW my_dataset.my_materialized_view AS: This part of the statement creates a new materialized view named my_materialized_view in the my_dataset dataset.
• SELECT user_id, COUNT(*) AS total_purchases, SUM(amount) AS total_amount FROM my_dataset.purchases GROUP BY user_id;: This query calculates the total number of purchases and the total amount spent by each user.

Once a materialized view is created, you can query it just like a regular table or view:

• SELECT user_id, total_purchases, total_amount FROM my_dataset.my_materialized_view WHERE total_purchases > 10;: This query retrieves the user IDs, total purchases, and total amount for users who have made more than 10 purchases.

Materialized views need to be refreshed to ensure they contain up-to-date data. BigQuery supports both manual and automatic refreshes.

You can manually refresh a materialized view using the REFRESH MATERIALIZED VIEW statement:

BigQuery can automatically refresh materialized views based on a defined schedule. This can be set up using the OPTIONS clause when creating the materialized view:

• enable_refresh = true: Enables automatic refresh for the materialized view.
• refresh_interval_minutes = 60: Sets the refresh interval to 60 minutes.

• Use for Complex Queries: Materialized views are ideal for complex queries that are frequently executed.
• Monitor Refresh Costs: Be mindful of the costs associated with refreshing materialized views, especially if the underlying data changes frequently.
• Optimize Query Performance: Ensure that the queries used to create materialized views are optimized for performance.

• Aggregated Reporting: Use materialized views to precompute aggregated data for reporting purposes.
• Data Warehousing: Materialized views can be used to store precomputed results of ETL (Extract, Transform, Load) processes.
• Real-Time Analytics: Use materialized views to provide near real-time analytics by setting appropriate refresh intervals.

Materialized views in BigQuery offer a powerful way to improve query performance and reduce costs by precomputing and storing query results. By understanding how to create, query, and maintain materialized views, you can leverage this feature to optimize your BigQuery workflows. Remember to follow best practices and consider the specific use cases where materialized views can provide the most benefit.