Tuesday, July 31, 2012

Harness Big Data with Google BigQuery

Google’s service called BigQuery allows you to run SQL-like queries against large datasets on a subscription basis.

These days, even the smallest of enterprises have amassed terabytes-if not petabytes-of data storage. Additionally, services like YouTube and Twitter are said to surpass terabytes of data daily. There is no doubt about it; we are in the age of “big data”. So the question arises, how are we to scan these vast heaps of information without experiencing the performance degradation noticed with relational database servers (mostly due to their propensity to preserve a relational structure, or attain what is known as normalization)?

Google BigQuery is essentially an on-demand big data storage and querying service. One can store as much data as he/she feels necessary, and only have to pay for what he/she uses (to the extent of certain storage limits). Furthermore, one can scale his/her data to the amount of hundreds of terabytes of data, with no additional management needed. Users manage their data stores through a web-based interface, or alternatively through a HTTP REST API or by way of command-line. This includes the running of SQL-like queries that not only allows one to query columnar data structures, but also join related tables, just as one might do with a traditional SQL database engine. More details here 


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