Google today is announcing the launch of new features for its BigQuery cloud data warehouse service.
Since BigQuery became available to all developers in 2012, it’s been possible to query data with the SQL-like BQL, which stands for BigQuery Query Language. Now Google is bringing full SQL to the service, and that’s significant because of the pervasiveness of ANSI SQL, which is the lingua franca for data analysts.
Now people can use theta joins in BigQuery, and dates, times, arrays, and timestamps are now supported, Google technical lead and manager Dan Delorey and technical product marketing manager Bosco Zubiaga wrote in a blog post. Analysts can also submit more detailed subqueries within any part of a query, wrote Delorey and Zubiaga.
Google is also bringing the Cloud identity and access management (IAM) feature of Google Cloud Platform to BigQuery.
And now analysts have a way to get better performance by limiting queries to certain time periods. The feature can be viewed as an answer to the Table Partitioning feature of Microsoft’s SQL Server database software.
But more generally, these additions make BigQuery a slightly better choice as a standalone querying service. Clearly, Google is trying to make the service more competitive with the Redshift service from Amazon Web Services (AWS), which is considerably farther ahead of the Google Cloud Platform in the public cloud market. Other services in this category include Microsoft’s Azure SQL Data Warehouse and Snowflake.