7 Tips for Saving Money in BigQuery

How to reduce Costs when working with Google’s Data Warehouse

Google BigQuery is a SaaS and Google Cloud based Data Warehouse and Analytics Platform that enables users to store, manage and analyze vast amounts of data in a quick and efficient manner. With its countless benefits for companies, its usage can also be quite expensive. In this article, I would like to feature seven tips for how to reduce costs and save money when working with Google BigQuery.

Tip 1: Optimize your Data Storage.

One of the most effective ways to reduce costs when using BigQuery is to optimize the data storage. BigQuery charges its users for both the amount of data stored in the platform as well as the amount of data processed. Therefore, minimizing the stored data in BigQuery can result in significant cost savings. You could achieve this minimization by simply deleting unnecessary or no longer needed data. You can also outsource large tables, for example, by storing unnecessary data in a new table and archiving it, so to speak. The queries are then only executed on the remaining data, which reduces query costs.

Tip 2: Partition and Cluster your Tables.

Partitioning tables in BigQuery is another way of how to optimize your data storage. Simply divide a bigger table into various smaller ones that have more manageable parts. As a result, the smaller tables are easier to query and therefore analyze. By partitioning tables, you can easily reduce the amount of data that has been processed and thus save valuable monetary assets of your company. Like in other databases it’s also useful to create indexing. You can use indexes to improve query performance, for example, by creating clustered indexes for frequently searched columns. Here is an article with more insights to this topic[1].

Tip 3: Make use of columnar Data Storage.

BigQuery usually utilizes column oriented data storage, where data is stored in a column oriented format rather than a row oriented format. As a result, the Data Warehouse solution is able to query faster and to reduce processed data. Here you can use nested data field or also the JSON data type which was made available last year[2].

Tip 4: Utilize cost-effective Storage Options.

BigQuery offers various storage options such as regional and multi-regional storage. While regional storage is cheaper than multi-regional storage, you must take into consideration whether the first option might be the fitting storage option for your needs, since in some cases data availability across multiple regions can be also crucial for your company. Hence, you have to evaluate your business needs beforehand and choose the storage option that meets your requirements and budget in the best possible way.

Tip 5: Control your Data Processing Costs.

It is known that BigQuery charges users for the amount of data processed. Therefore, controlling your data processing costs is crucial for detecting possible flaws in the product chain and reduce costs. One way to reach this goal is to avoid unnecessary data processing. An example: You have to analyze data from a certain period of time. Hence, you use filters that exclude data that is not relevant for the specific time period in order to avoid unwanted data processing.

Tip 6: Use cached Queries.

BigQuery caches frequently used queries, which make it possible for you to save query processing costs, since cached queries are free, and you can reuse them without any additional costs.

Tip 7: Use BigQuery Reservations and Query Queues

BigQuery provides reservations that make it possible for its users to buy capacity beforehand. By doing so, you can save up to 50% on data processing costs. These reservations can be used for data processing at any given time for the previously purchased amount of data processing capacity. Together with Query Queues it can be a good option, without users having to accept too many restrictions. When you enable query queues for an on-demand project or reservation, BigQuery automatically determines the number of queries that can run concurrently.

Conclusion

In conclusion, Google BigQuery can be a powerful tool for companies and organizations, but it can be very expensive in its usage. However, if you take the previously seven tips into consideration, you can indeed lower your BigQuery costs significantly and save valuable monetary assets.

Sources and Further Readings

[1] Google, BigQuery explained: Storage overview, and how to partition and cluster your data for optimal performance (2023)

[2] Google, Working with JSON data in Standard SQL (2022)

[3] Google, Use query queues (2022)