BigQuery Partitioning & Clustering | by Alessandro Puccetti | Analytics Vidhya | Medium

In this blogpost, I will explain what partitioning and clustering features in BigQuery are and how to supercharge your query performance and reduce query costs. Partitioning a table can make your queries run faster while spending less. Until December 2019, BigQuery supported table partitioning only using date data type. Now, you can do it on integer ranges too. If you want to know more about partitioning your tables this way, check out this great blogpost by Guillaume Blaquiere. Here, I will focus on date type partitioning. You can partition your data using 2 main strategies: on the one hand you can use a table column, and on the other, you can use the data time of ingestion. This approach is particularly useful when you have very large datasets that go back in time for many years. In fact, if you want to run analytics only for specific time periods, partitioning your table by time allows BigQuery to read and process only the rows of that particular time span. Thus, your queries will r

In this blogpost, I will explain what partitioning and clustering features in BigQuery are and how to supercharge your query performance and reduce query costs. Partitioning a table can make your queries run faster while spending less. Until December 2019, BigQuery supported table partitioning only using date data type. Now, you can do it on integer ranges too. If you want to know more about partitioning your tables this way, check out this great blogpost by Guillaume Blaquiere. Here, I will focus on date type partitioning. You can partition your data using 2 main strategies: on the one hand y