BigQuery users can now create and run Spark stored procedures using BigQuery APIs, allowing them to extend their queries with Spark-based data processing
BigQuery data processing beyond SQL may require Spark-based business logic or Apache Spark expertise
To create, test, and implement your PySpark code, BigQuery Studio offers a Python editor as part of its unified interface for all data practitioners
After testing, the process is kept in a BigQuery dataset, and it can be accessed and controlled in the same way as your SQL procedures
BigQuery Spark stored procedures can be configured to install packages required for code execution
For data science, data engineering, and machine learning on single-node computers or clusters, Apache Spark is a multi-language engine
On a laptop, train machine learning algorithms, and then use the same code to scale to thousands of machines in fault-tolerant cluster
An advanced distributed SQL engine for large-scale data is the foundation of Apache Spark