From Small to Big Data, Adopting the Advanced Analytics Mindset

Our objective at Dataiku is to create a tool that helps data teams - analysts, scientists, and managers - to collaborate on small to big data projects. One of the key success factors for these teams is to allow analysts to work on Big Data as easily as they do on smaller data with Excel, as well as to help them find new use cases specific to the data available and the tools at hand. This document is not intended to be all-encompassing, but it should provide a taste for how Dataiku DSS works relative to Excel. It’s divided into three parts:
1.A brief introduction to Dataiku DSS concepts and terminology
2. From Excel to Dataiku DSS: a mapping of usual Excel functions
3.A couple examples of Dataiku DSS features that do not exist in Excel

If you’re used to using Excel, you probably find it pretty easy to do a lot of things with it - you might even have developed a certain fondness for it (well, maybe that’s a stretch). But the problem is that data keeps getting bigger and bigger, often too big for Excel to handle without freezing and crashing. Furthermore, data resides in all sorts of places, and it’s not uncommon to find yourself dependent on someone else (maybe IT or a data scientist) to connect to and prepare the data for you.
Dataiku DSS gives you the power to do the connecting and preparing yourself, and then to do all your analysis on any dataset, no matter what the size. Thanks to a user-friendly graphical interface, there’s no coding required to do nearly everything you do in Excel. And Dataiku’s collaboration features mean that you can always work together with data scientists and business experts on your project.
This Guidebook is not meant to be all-encompassing, but rather a little appetizer of how Dataiku DSS works from the point of view of an Excel user. You’ll see how to perform some common Excel
functions in Dataiku, and then you’ll see a couple of examples of Daitiku features that don’t even exist in Excel.