Tuesday, 26 January 2016

Google BigQuery- An externalized version of Dremel


So What is Google Big Query?? Its powerful Big Data analytics platform used by all types of organizations to run SQL-like queries against multiple terabytes of data in a matter of seconds. With this cloud based interactive query service we can handle web-sized amounts of data at blazing fast speed. 


Big Query (released in 2010)is actually the external or public implementation of one of the Google’s core technologies so-called Dremel .Big Query provides the features available in Dermel to third party conserving its unparalleled query performance. Both in fact share the same underlying architecture and performance characteristics. 


Big Query release made it possible to utilize the power of Dremel and to take advantage of Google’s massive computational infrastructure.

Let’s take a deeper look into power of Dremel… It is a query service that allows you to run SQL-like queries against very, very large data sets and get accurate results in mere seconds. You just need a basic knowledge of SQL to query extremely large datasets in an ad hoc manner.

Dermel runs through tens of thousands of servers simultaneously and makes it easy to analyse large amount of data such as a collection of web documents or a library of digital books or even the data describing millions of spam messages.

“According to Google’s paper, this has been used inside Google since 2006, with “thousands” of Googlers using it to analyse everything from the software crash reports for various Google services to the behavior of disks inside the company’s data centers”


The two core technologies that makes Dremel and BigQuery so fast is the Tree Architecture of Dremel And that the Data is stored in a Columnar Storage fashion in so doing, it gives very high compression ratio and scan throughput. 




So how to use data in Big Query or how to import data into Big Query:

  • Upload your data to Google Cloud Storage
  • Import the files to Big Query. Executed using command-line tool, Web UI or API, which can typically import roughly 100 GB within a half hour.

Other Important Features of Google Big Query:
  • BigQuery is designed to handle structured data using SQL. Apart from SQL queries we can easily read and write data in Big Query via Cloud Dataflow, Spark, and Hadoop
  • BigQuery provides extremely high cost effectiveness and full-scan performance for ad hoc queries and cost effectiveness compared to traditional data warehouse solutions and appliances.
  • BigQuery is the best choice for ad hoc OLAP/BI queries that require results as fast as possible.
  • BigQuery requires no capacity planning, provisioning, 24x7 monitoring or operations, nor does it require manual security patch updates. You simply upload datasets to Google Cloud Storage of your account, import them into Big Query, and let Google’s experts manage the rest.

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Please go through our latest post TOP 6 BIG DATA TRENDS IN THE NEAR FUTURE

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