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Google made its BigQuery service public on May 1st. Though the name sounds like “Big Data,” Google’s offering is really a self-service Business Intelligence (BI) solution, hosted in the cloud. In this gallery, I'll tell you how it works.
BigQuery is a cloud-based data analytics system from Google. It lets you upload data, then analyze it using SQL (Structured Query Language) as the query interface. BigQuery lets you query up to 100GB of data per month for free. Just create a Google Developer account for yourself, create a new project and enable BigQuery within it.
Click on any image to enlarge.
BigQuery offers a simple, easy to master browser console, providing for dataset browsing on the left and SQL querying on the right. When queries are returned, options appear to let you save the results in alocal CSV file or create a new table containing the result set’s content.
A simple query against the sample github_timeline table is shown here. The first few rows from the results of the query appear on the bottom-right of the screen, along with navigation controls that allow you to page through the data. Note the “Save as Table” and “Download as CSV” options, which work nicely in Chrome and FireFox. Unfortunately, the "Save as Table" option is not available in Internet Explorer (nor is a file upload option we'll look at shortly). Everything in BigQuery also works nicely in Safari on the iPad, though you can’t save or upload local files there either.
BigQuery data is stored in tables, much as in a relational database. Tables, in turn, are stored within datasets. Datasets serve as a unit of security, allowing for sharing with specific users or the overall public. Google supplies the publicdata:samples dataset which is added to every BigQuery project. This allows you to examine and query tables right away.
At the left side of the screen, you can drill down on a dataset to see the tables it contains. Select one of those tables and its schema will appear on the right-hand side of the console. The github_timeline table’s schema is displayed in this figure. Notice that its label on the left side of the screen appears in bold text with a red bar beside it. The “Click to preview table data” link does what it says, but you can also write your own SQL queries.