Hack your Data Warehouse by Using Notebooks

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Notebooks are great for doing exploratory analytics iteratively over large and complex data sets. The emphasis here is on iterative exploration — letting you construct and run queries interactively over and over, building on previous queries and their results, until the exploration yields the insights you are after.

If you are using a next gen big data platform like Apache Spark, you already know the value of using notebook style analytics. Good news for dashDB users is that you too can now easily leverage the power of IPython or Jupyter notebooks to hack data in your warehouse interactively.

To get started, first you need to get your free access to Data Scientist Workbench by signing up at:

Data Scientist Workbench provides a browser-based notebook as a service environment and supports R, Python, and Scala. So there is nothing to install on your workstation or maintain. New features are being constantly added and automatically become available for you to use. Just load the Workbench in your browser, and within seconds you can be connected to and hacking data in your dashDB warehouse.


A Hacking Data blog post contains the step-by-step instructions for connecting the Data Scientist Workbench to dashDB. But, frankly, it takes longer to read the post than to actually do it. Have fun hacking 😉

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