In the midst of the Information Age, data is one of the most precious commodities for businesses hoping to succeed in an increasingly competitive economy. So a robust class of solutions has evolved to provide tools for collecting, analyzing and interpreting data and ensuring it is put to good use.

Google (News - Alert) has joined the party with Cloud Datalab, a new interactive tool based on the Google Cloud Platform and running on the Google App Engine for large-scale data exploration, analysis and visualization. Built on the Jupyter platform, Cloud Datalab is built primarily for dealing with data on Google BigQuery, Google Compute Engine and Google Cloud Storage through the use of Python, SQL and JavaScript functions. Processed data can then be used for a variety of applications such as deploying data pipelines to BigQuery or creating machine learning models.

The tool’s notebook format — enabled by Jupyter — combines code, documentation, visualizations and results into a single format for a more intuitive approach that is fully integrated with the aforementioned data sources. Google Charts or matplotlib can be used for visualizations, while the git-based source control of these notebooks means it can be synchronized with non-Google source code repositories such as GitHub and Bitbucket and enable direct collaboration. It’s actually hosted on GitHub as an open source project, so developers will be able to easily extend the product’s functionality while deploying it according to custom specifications.

Because it is still in its beta period, Google Cloud Datalab can be deployed as an App Engine application for free; the actual pricing through this platform once it’s officially launched will be unveiled further down the road. The tool is intuitive and useful but not exactly user-friendly for non-developers, so there is a limit to who will benefit from the tool but those who do will surely appreciate its integration with other Google products and innovative approach to data processing and visualization.