Hoping to lure more Apache Hadoop users to its own data analysis services, Google has outfitted BigQuery with the ability to query multiple data tables.
"Joining terabyte-sized tables has traditionally been a challenging task for data analysts, requiring sophisticated MapReduce development skills, powerful hardware, or a lot of time -- often all three," wrote Ju-kay Kwek, Google BigQuery product manager, in a blog post announcing the update. "Today with BigQuery you can get directly to business insights using SQL-like queries, with far less effort and far greater speed than you could before."
[ Andrew C. Oliver answers the question on everyone's mind: Which freaking database should I use? | Keep up with the latest approaches to managing information overload and compliance in InfoWorld's Enterprise Data Explosion Digital Spotlight. ]
Google also argued that using BigQuery instead of a Hadoop deployment will save users money, because they only pay for the queries that are processed, rather than pay for the computational costs of running individual Hadoop supporting components.
Launched in 2010, BigQuery has been marketed by Google as an interactive service for parsing large amounts of data. With BigQuery, a user submits a data set to Google, then can query the data through the BigQuery API (application programming interface).
The new updates expand capabilities BigQuery already has in place. Most notably, a new JOIN clause that combines the results of a query across multiple data sources. Prior to this update, BigQuery's JOIN clause could only work with a data set less than 8MB in size. The new clause, JOIN EACH, has no limit on the size of the data.
As a result, the service can now be more effectively used as a replacement to Hadoop's MapReduce. Many Hadoop jobs are designed to bring together large amounts of data from two or more data sets. To do this however, developers must write MapReduce processes from scratch, which can be time consuming. JOIN EACH can produce a single result set from two large database tables that share a common key.
"With these capabilities, you will now be able to join and perform aggregate analysis on multi-terabyte datasets using SQL-like queries or integrated [third] party tools, instead of having to initiate complex coding projects," wrote Michael Manoochehri, Google's cloud platform developer programs engineer, in a technical blog post explaining the update.
BigQuery also now offers a better way to group query results as well. The GROUP BY EACH statement increases the number of distinct entities that can be grouped in a result set, though at a potential cost to processing performance.
The BigQuery update includes a couple of other new features as well. The service has more supports for timestamps: BigData can now import timestamps from other systems, as well as query timestamp data. Users can now add columns onto existing tables. Users can now also bookmark the specific datasets they have access to, as well as receive automated emails when they have been given access to a new dataset.