"Self-service analytics" has always been a questionable ideal -- of course you want to enable analysts and non-technical users to work with data on their own, but you still want their work to follow the best practices laid out by data scientists and other experts in your organization.
Here at Dataiku, we like to emphasize the importance of everyone working together on the same open platform so that users can learn from one another and easily check each others' work. That said, there are some basic steps of data preparation that can and should be done by anybody on their own without having to take to Slack to ask for a SQL expert.
In this second installment of introductions to our Visual Recipe video series, we'll look at three of the most popular data prep solutions in Dataiku: the Join Recipe, the Group Recipe, and the Split Recipe.
The first video shows the basics of the Join Recipe, which joins two databases (which can be different types of databases) according to your conditions. Sometimes some of the best insights come from combining one dataset with another, so being able to do this easily and quickly is a big advantage.
The second video shows how the Group Recipe works. Essentially, the Group Recipe replicates the "group by" statement in SQL -- it lets you aggregate data based on the field or fields that you choose.
And the third video explains the Split Recipe -- once you've joined datasets together, you might want to split your dataset into smaller ones. The Split Recipe makes this easy not only to do, but also it makes it easy to decide where the new datasets will reside.