Original URL: http://www.theregister.co.uk/2013/03/15/google_adds_sql_to_bigquery/

Google makes BigQuery easier to question

SQL commands let devs probe BigQuery AaaS

By Jack Clark

Posted in Cloud, 15th March 2013 00:56 GMT

Google has updated its BigQuery cloud analytic service to make it attractive to people familiar with SQL.

The new features for the analytics-as-a-service (AaaS) – Big JOIN, Big Group Aggregations, and support for the TIMESTAMP data type – were released by Google on Thursday.

They are designed to cut the steps developers need to take in analysing large amounts of data, and make the technology easier to deal with.

"In response to developer feedback, we're launching new features that enable analysts and developers to run fast SQL-like join and aggregate queries on datasets without the need for batch-based processing," Michael Manoochehri a developer programs engineer for the Google cloud platform, wrote.

Google, like many other tech pioneers, has spent the past half-decade bingeing on NoSQL systems, but like any reformed glutton has realized that this may not have been wise.

The BigQuery update can be considered another way the Chocolate Factory is paying penance for its binge, alongside its move back to SQL-like systems on its internal platform with the introduction of Spanner and F1.

Previously, BigQuery made developers run analytics atop a single (very large) table. But as many people keep their data in all sorts of different tables this made porting stuff into BigQuery painful.

Google has now done away with this, and the analytics service lets developers use standard JOIN functions to link multiple tables of data together.

"Big JOIN simplifies data analysis that would otherwise require a data transformation step, by allowing users to specify JOIN operations using SQL," Manoochehri wrote.

Google has also launched Big Group Aggregations, which lets you put a larger number of distinct values into a result set. In a further bout of SQL-slathering, timestamp data can now be imported as well, he wrote.

Google launched BigQuery in a beta in 2011, and the AaaS became available for general prodding in May 2012. It can support queries across hundreds of terabytes, but so far Google has not provided developers with a feasible way to actually get that much data into its cloud.

The updates all go to make BigQuery more attractive to developers – a key consideration for Google as it tries to generate enthusiasm for its cloud platform while fighting rivals Amazon and Microsoft on price on multiple fronts. ®