BigQuery cost optimization refers to the process of minimizing unnecessary expenditures while still meeting the performance and data analysis requirements.
Contrary to popular belief, Google BigQuery is a cost-effective solution. However, it can get expensive pretty fast in the hands of a rookie.
BigQuery Cost Optimization best practices.
The following are the best practices when it comes to reducing BigQuery costs:
#1 Practice data minimization.
Data minimization is the practice of collecting, storing and using only the personal data which you absolutely need for the purpose you have specified in your privacy policy.
Collecting unnecessary data about website users and customers can violate the General Data Protection Regulation (GDPR) rules.
Other than the privacy benefits, implementing data minimization techniques can help reduce the cost of using BigQuery.
When you minimize data collection, you only retain and process the essential information required for your data analysis or business operations.
By removing redundant or obsolete data, you can reduce the storage space needed in BigQuery, thereby lowering data storage costs.
Additionally, minimizing the volume of data being processed can decrease the amount of data scanned during queries, reducing query costs.
One of the biggest complaints I often hear about GA4 BigQuery usage is exceeding the daily BigQuery export limits.
These limits are sufficient for some businesses to give up on GA4 entirely.
That’s why you must evaluate your tracking requirements seriously.
Do not collect unnecessary event data, esp. at the expense of business-critical information.
Audit your GA4 property to identify and remove events that are not business-critical.
The best practice is to minimize the number of events you track so you don’t easily hit the BigQuery export limits.
#2 Avoid mindless data processing.
Mindless data processing is indiscriminate or excessive data processing without a clear objective.
If you regularly find yourself testing the limits of Google Sheets or MS Excel, you are most likely not ready for BigQuery.
Because that means you have a habit of mindlessly processing large amounts of data.
You do not have clearly defined data analysis objectives. You do not have clearly defined business questions.
Most people download a large chunk of data and then decide what to do with it. You can get away with this bad habit when using Google Sheets/Excel.
What’s the worst that could happen? Your application will freeze.
But what’s the worst that could happen when you bring your bad habit to BigQuery?
BigQuery will charge your company dearly for mindless data processing. You could end up paying hundreds or thousands of dollars to Google each month.
When you engage in mindful data processing, you carefully consider the data you query in BigQuery. You avoid unnecessary joins, aggregations, or excessive data transformations.
