Hi, data enthusiasts.
I am sure you have run into a number of problems when utilising hard coding to handle data. The data you downloaded from websites or from any database could be quite untidy. Alteryx, on the other hand, will allow you to perform things quickly and easily without having to code! As a new Alteryx user, I was blown away by this tool because it allowed me to construct a simple workflow rather than spending hours in coding and debugging!
So, what is Alteryx?
Well, It is comparable to SSIS, but with a lot more user-friendly interface, faster processing times, and more features. It is made for data preparation, prescriptive analysis, and even coding-free machine learning, etc. What’s more, it is an excellent Tableau partner because it wraps up pre-baked connecting choices. So, if you are passionate about data visualisation, you should definitely check it out.
Now it’s time to show you some basic Alteryx tools that are quite simple to use.
Data type adjustment
When looking into a dataset, the data type is usually the first thing we need to know. In many cases, it will cause lots of issues when data types are not appropriate. For example, it requires the numerical data type when doing the calculation on some specific fields, but we may encounter the case to get the surprising results if those fields are the String type.
Alteryx makes it easy to check the data type as well as change the data type. By simply clicking “metadata” in the output window, the information for each field will appear. To adjust the data type, we can just drag the “Selection” tool, which allows us to change anything we need ( the data type, the name of the fields). Alternatively, we can utilise the “Auto field” feature to update the data type automatically.
Text to columns
I am pretty sure sometimes you will encounter the problems of having a field containing multiple fields and they might be separated by some specific delimiters.
In Python, you probably need to do something like this:
Although the libraries will help you to do the task more quickly than before, you will still need time to set up the environments and debug any issues that arise. All we need to do in Alteryx is drag the tool “Text To Columns” into your workflow, and we’ll handle the delimiters on our own. What’s more, it will be much easier to change the name of the field using our “Select” tool as mentioned earlier.
Filter is a useful tool for extracting the information we require from a dataset. For example, suppose we have a dataset with a large amount of historical data dating back to 1980. But we only require information from February 2019. If we use the original data, not only will the enormous amount of information be confusing, but our pipeline’s performance would be significantly hindered.
So, it is time to introduce the “Filter” in Alteryx, all you need to do is to select the field you want to specialize the data range you want to select from the data. The custom filter will also allow you to write your own functions so it is actually flexible like what we can do in coding.
There are so many amazing tools and advanced techniques all embedded in Alteryx. It requires time to learn and explore, but it will save you a lot of time when comparing with building a data warehouse.
I will introduce you more techniques later on in Alteryx, feel free to drop any feedbacks. Cheers everyone!