One of the most common tasks in any ETL project is converting data types. When data begins in the form of a text file or (even worse) a formatted excel file, one often has to load the data as text fields initially and convert to proper data types after parsing out the relevant data.
Thankfully Alteryx has a lot of built in features to facilitate this. One is the Multi-Field tool of which I gave an overview in my earlier post. This handy tool lets you apply the same function across a whole set of columns without having to laboriously replicate it separately for each column as you must do in SQL select statements.
In many cases, however, one doesn’t even need to use any explicit data conversion tool in Alteryx – instead, you can just use the handy select tool which let’s you easily specify the data type you want while also allowing you to rename fields. In the screenshot below, I’m using a select tool to convert generic field names (i.e. Field_1, Field_2, etc.) into more descriptive fields while at the same time converting them to the desired data type.
While this tool works great most of the time, one always seems to run into a catch eventually as I did the other day. Continue reading
While Alteryx has many great functions right out of the box, I realized one thing they’re lacking is an increment function (or at least I haven’t found one). Anyone who’s worked with SQL is familiar with the Identity data type that allows you to specify a seed and an increment value as follows:
IDENTITY [ ( seed , increment ) ]
This allows you to create a column that automatically generates new, unique integers by incrementing the starting “seed” value.
Occasionally when loading text-based keys or natural keys into a normalized database it’s helpful to be able to generate incremented IDs. Below is a snapshot of how I was able to accomplish this in Alteryx using the multi-row function.
Increment in Alteryx
The first data input is simply Continue reading
One common task most analysts encounter is the need to setup dynamic filters in their data workflows. In SQL this is usually accomplished via a sub-query which is joined in to a main query. In Alteryx, where each step is accomplished via dedicated tools or nodes, the process is similar but much more visual. Usually it involves some combination of the summarize, join, and filter tools.
Here at the TABS Group, we process lots of CPG sales data. Sometimes it comes in a weekly format and we often apply a threshold to remove items which have little to no sales over a two year period. Below is an example how this is easily accomplished via Alteryx.
Dynamic filter example in Alteryx
The data begins in the Continue reading
I thought I’d start off with an overall review of the Alteryx ETL tool and why I chose to give it a try.
If I had to summarize it in one word (well …two) it would be Easy/Productive.
I’d tried several ETL tools in the past including Pentaho’s Kettle tool and of course Microsoft’s SSIS tool. Both of which I found Continue reading
Hey everyone, this is post number one with many more to hopefully come soon. Check out my bio to get a little background on myself. I work with all kinds of data every day and hope to post helpful tips and tricks as I continue to learn myself. At this point I’d consider myself an expert Excel developer, intermediate in MS SQL, and a beginner in Alteryx and Tableau (Both incredibly useful and intuitive data blending/analytics tools).
Since I’m on an Alteryx kick right now, you might see a disproportionate number of posts on that 😉