Become a BigQuery pro by knowing the basics
Often, we’re eager to tackle complex challenges in programming, but laying a solid foundation in the fundamentals ensures our more advanced code functions seamlessly. No matter how long you’ve been writing SQL, it’s easy to overlook the basics.
With that in mind, In this series of 10 blogs, I would like to share my favourite tips for mastering BigQuery like a pro. These insights aim to reconnect you with essential practices that might have slipped your mind, ensuring your projects are not just ambitious but also impeccably executed.
1:BigQuery Dremel
BigQuery’s exceptional speed in processing massive queries is largely due to Dremel, its foundational technology. Dremel enables BigQuery to process data by columns rather than by rows, which is a departure from the row-based data storage you might be accustomed to with SQL Server and other databases. This columnar processing means that data retrieval is optimised for speed and efficiency, particularly beneficial for analytical queries.
However, to fully leverage BigQuery’s capabilities, it’s crucial to adapt your approach to data management. This includes minimising the number of columns your queries read— as each additional column can affect performance — and reconsidering the reliance on common SQL functions like LIMIT, which does not have the same performance implications in a columnar storage context. By aligning your practices with BigQuery’s strengths, you can achieve more efficient data…