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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.
To run a Query, type it in, then click the “RUN QUERY” button or just tap Ctrl-Enter on your keyboard. While the query is running, the query text area is disabled and the elapsed query time clock runs up, right next to the “Query running” label.
BigQuery does not permit “SELECT *”-style queries; instead, you must specify all column names. And although you’ll be querying large datasets, you will want to keep your result sets small. To do that, make use of aggregating queries (using aggregate functions and GROUP BY) and/or the LIMIT n clause at the end of your query as was done here (i.e. “LIMIT 200” appears at the end of the query).
Tables are identified using a syntax of datasetname.tablename. If you reference any table from the samples dataset, you’ll need to use the “publicdata:” prefix before the “samples” dataset name.
BigQuery isn’t just for big desktop and laptop computers. It runs very well on the iPad, for example, as shown here. And BigQuery is hip to tablet use too: rotate your iPad from landscape to portrait and the page rendering adjusts, showing you several extra rows of data (illustrated nicely here).