Google's BigQuery is a popular data warehouse solution known for its speed and scalability. Despite this, there are several ways to further enhance performance and manage costs effectively.

Performance Optimization #

Schema Design #

Optimizing the design of your schema can significantly improve query performance. Use nested and repeated fields to flatten your data, minimizing the need for JOIN operations.

Use of Partitions and Clusters #

Partitions and clusters allow for more efficient data segmentation. By partitioning your data on frequently filtered columns, and clustering by columns used in WHERE, JOIN, and ORDER BY clauses, you can minimize the amount of data scanned in each query, thus enhancing speed and reducing cost.

Cache Use #

BigQuery automatically caches query results for 24 hours. Frequently accessed data can be retrieved from the cache, reducing the amount of data read and hence improving performance.

Cost Optimization #

On-Demand and Flat-Rate Pricing #

BigQuery offers two pricing models - on-demand and flat-rate. On-demand charges you for the amount of data processed by each query, while flat-rate offers unlimited queries for a fixed price. Selecting the right model for your use case is key to managing costs.

Use of Preview #

Use the preview option instead of running a full query when you just need to view a small part of the dataset. This operation is free of charge.

Use Cost Controls #

Set custom quotas and alerts to manage your costs. BigQuery provides features to set daily limits and to send notifications when you are close to reaching your specified quota.

Remember that performance and cost optimization in BigQuery is a continuous process, requiring regular monitoring and adjustment as your data and business needs evolve.