Google on Thursday announced a slew of new features for Google BigQuery, its service for quickly analyzing large amounts of data, to let analytic teams deliver what organizations really need: “actionable and data-driven business insights.” In short, Google has added new capabilities to help businesses work effectively with large amounts of data over a greater range of query and data types.
Here are the three new features Google wants to highlight:
- Big JOIN: use SQL-like queries to join very large datasets at interactive speeds.
- Big Group Aggregations: perform groupings on large numbers of distinct values.
- Timestamp: native support for importing and querying Timestamp data.
The new Big JOIN feature gives users the ability to produce a result set by merging data from two large tables by a common key: you can skip a data transformation step by simply specifying JOIN operations using SQL. Big Group Aggregations meanwhile significantly increase the number of distinct values that can be grouped in a result set. To use these two new features, all you have to do is add the EACH modifier to JOIN or GROUP BY clauses.
The new TIMESTAMP data type lets you import date and time values in formats familiar to users of databases such as MySQL, while still preserving timezone offset information. There are also new functions for converting these fields into other formats, calculating intervals, and extracting components such as the hour, day of week, and quarter.
Google has also added the ability to add new columns to existing BigQuery tables. To do so, provide a new schema with additional columns using either the “Tables: update” or “Tables: patch” BigQuery API methods.
Last but not least, there are now direct links to individual datasets in the BigQuery Web UI so authorized users can quickly access a dataset, and bookmark it or share it. Email notifications have also been added to inform users when they’ve been given dataset access privileges:
Google explains that these features working in conjunction let you join and perform aggregate analysis on multi-terabyte datasets using SQL-like queries or integrated third-party tools. Without them, the company argues you’d have to initiate complex coding projects, which of course cost both time and money.
In fact, Google is eating its own dog food when it comes to Big Query:
For example, when our App Engine team needed to reconcile app billing and usage information, Big JOIN allowed the team to merge 2TB of usage data with 10GB of configuration data in 60 seconds. Big Group Aggregations enabled them to immediately segment those results by customer.
It’s difficult to argue with figures like that.
See also – Google Cloud Platform gets new storage options, 20% price cut, more European datacenter support and Google debuts four-tiered 24/7 support for its cloud platform services, prices start at $0 to $400 per month
Image credit: Pawel Kryj