Google BigQuery: Running SQL-like Queries Against Very Large Datasets

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Announced at GigaOm Structure Data event, Google launches a new BigData service named BigQuery:

BigQuery enables businesses and developers to gain real-time business insights from massive amounts of data without any upfront hardware or software investments.

A quick bullet point list of BigQuery features and limitations:

BigQuery is ideal for running queries over vast amounts of data—up to billions of rows—with great speed.
BigQuery is good for analyzing vast quantities of data quickly, but not for modifying it. In data analysis terms, BigQuery is an OLAP (online analytical processing) system.
You can import data into BigQuery as CSV data, where it is stored in the cloud in a relatively small number of tables with no explicit relationship to each other.
BigQuery isn’t a database system:
- It doesn’t support table indexes or other database management features.
- BigQuery supports a specialized subset of SQL; it doesn’t support update or delete requests.
- BigQuery supports joins only when one side of the join is much smaller than the other.
BigQuery can be used by any client able to send REST commands over the Internet.

After the break you can watch the 15 minutes video recorded at the GigaOm event.