After six months in preview, Google has finally released BigQuery, a Big Data tool that enablesusers to query and process large datasets on Google’s cloud.
Unlike previous Google Big Data innovations such as the MapReduce framework, Google has commercial aspirations for BigQuery, adding value to its Google Apps and Google Analytics offerings. BigQuery provides a much easier to use, higher performance alternative to clients of “raw” infrastructure-as-a-service (IaaS) offerings such as Amazon’s Elastic MapReduce.
But there are several caveats. Like any cloud service for Big Data, it is questionable whether customers will be willing to deploy their data in public clouds, and while Google is renowned for web innovation, the visualization capabilities of BigQuery appear to be surprisingly arcane. The main benefit will be its ability to analyze and enhance the effectiveness of web interactions, makingit highly complementary to Google Analytics. For the SQL developer, the tool will also function as a way into Big Data querying and analytics, a field that is waiting for talent to join.
Google’s take on Advanced SQL
BigQuery is basically an IaaS solution that enables users to upload, query, and analyze large datasets in the Google cloud. Like any cloud-based solution, it allows users to escape the hassle and expense of deploying and maintaining a large on-premise processing infrastructure. Through SQL-like queries, data can be queried through a web-browser, command line, REST API, and Google Apps scripts.
Google categorizes BigQuery as an “OLAP system”. In contrast to the company’s OLTP-focused Google Cloud SQL service, BigQuery does not offer full-SQL syntax and table management tools such as table indexes, updates, deletes, or other SQL data-management features.
BigQuery provides an SQL-like front end, making it friendlier to the large base of enterprise SQL developers, and allows higher performance than is associated with Hadoop. BigQuery is Google’s take on an Advanced SQL platform but without the SQL per se.