Google BigQuery Updated for Easier Data Analysis

The Google Developers Blog have announced that the Google BigQuery has been updated with a greater range of query and data types that adds more flexibility with table structure, and better tools for collaborative analysis. The Google BigQuery is Google's tool that lets your run SQL-like queries against large datasets. The tool is designed to work most effectively when used for interactive analysis of large datasets, typically using a small number of very large, append-only tables. The BigQuery improvements starts with the addition of Big Join and Big Group Aggregations. The Big Join cuts out the intermediate data transformation step. It lets you merge data from two large tables with a common key to produce data set. The Big Group Aggregation increases the number of distinct values that can be grouped in a result set, so you can set up queries on larger subsets of data. Here's the statement made by Michael Manoochehri, the Developers Program Engineer at Google Developers Blog. “Popular web applications produce user activity logs that can grow by billions of rows each week. Dividing users into smaller groups is a key step for analysis. However, each group of users can number in the millions. To handle this for such large volumes, we've enabled Big Group Aggregations.” The Google Developers Blog have also made a presentation example for the new changes made in Google BigQuery. Other changes in Google BigQuery which is minor in scope but will surely save developers great deal of work is the addition of native support for the TIMESTAMP data type. The Google BigQuery also improved the ability to add columns to existing BigQuery tables. Aside from the above-mentioned improvements, the BigQuery Web UI has also been improved. This improvement will let users to see direct links to individual datasets in the BigQuery Web UI. As programmers you can now signed up to BigQuery, so that you can test the new features using BigQuery's set of public datasets for free. For more information regarding the Google BigQuery updates just visit the official Google Developers Blog.