Clustering to Save Time and Money in BigQuery 2024
Welcome to part 2 of my in-depth series on maximizing efficiency in Google BigQuery.
For those who’ve followed the first part of my BigQuery series on table partitioning, this article is the perfect sequel. If you need a refresher or are looking for other topics, remember to refer back to my Ultimate Guide to Saving Time and Money with BigQuery article, which serves as the hub for all the in-depth guides in this series.
Understanding Table Clustering in BigQuery
Clustering in BigQuery is akin to adding another layer of organisation within each partitioned drawer of our data filing cabinet. Think of it as not only having your files sorted into different drawers by year or month but also having them grouped by specific characteristics within each drawer, like department or genre.
To paint a clearer picture, imagine each drawer in your filing cabinet represents a partition organised by month. Within these drawers, clustering arranges the files into neatly organised groups based on common attributes, such as customer region, product…
