by Pushkar Reddipalli
BigQuery Best Practices to Optimize Cost and Performance
BigQuery is Google's fully managed, petabyte scale, low cost analytics data warehouse. It allows you to execute terabyte-scale queries, get results in seconds, and gives you the benefits of being a fully managed solution. BigQuery and other Google Cloud Platform (GCP) services offer "pay per use" pricing. If you know how to write SQL Queries, you already know how to query it. In fact, there are plenty of interesting public data sets shared in BigQuery, ready to be queried by you.
In this blog post, I will guide you through best practices to implement Google BigQuery to control costs and optimize performance regardless of the level of development experience you have. To start working with BigQuery, you must first upload your data, which means that you will also use it as storage, then verify this data in some way to create your models or create reports. In the next section, we will analyze best practices for these main actions of loading, querying and storing.