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Managing Big Data with Google Cloud Datalab

Posted by Sandeep Chanda on Dec 1, 2015

The Google Cloud Datalab is Google's answer to big data exploration, analysis and visualization.

All cloud service providers are competing intensely to close the loop of offerings in the realm of big data services that includes exploration, transformation, analysis, and visualization services. In an earlier post, I mentioned the announcement of QuickSight, the data visualization platform from the Amazon Web Services team that now makes the platform an end-to-end provider of cloud-based real-time data stream analytics and visualization services, competing neck and neck with Microsoft Azure's Analytics Platform System. Guess what, Google is not far behind either!

Last week it announced the availability of Cloud Datalab, Google Cloud's platform for data exploration, analysis and visualization. The timing of this beta release couldn't have been sweeter as it comes closely on the heels of Google announcement of the open sourcing of its machine learning platform TensorFlow.

Datalab is extremely interactive and provides for an interactive notebook environment where you can store your analysis as notebooks and then publish and share your insights with the world. As a developer you can go further to develop, test, and deploy data processing pipelines. Support for BigQuery is most obvious. You can seamlessly write code in Python, SQL, and BigQuery UDF constructs to build and test your pipeline. Underlying it leverages Jupyter, a powerful web-based notebook platform for sharing documents containing live code. This becomes particularly useful for storing machine learning and statistical models. You can even reuse existing Jupyter notebooks.

To start using Datalab, you need to first register your application for Google Compute Engine.

If you don't have an account, you can sign-up for a free trial with $300 credit. If there are no existing projects in your developer console, a default "My Project" is created.

Once your billing is activated, you can start deploying your Cloud Datalab as an App Engine application.

As far as pricing is concerned, you only pay for the use of underlying cloud resources by the App Engine such as BigQuery and Storage. Competition is sure to get hotter with this release, and only time will pronounce the winner!


cloud services, Data Analytics, Google Analytics, big data analysis

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