In the last blog article from the Y42 series, we covered how to build data models using Y42. We discussed the steps involved in building pipelines using the Y42 UI model and how field nodes can be manipulated. Let's get right into how function nodes can further define our F1 data!
Sanu has already explained how you can create the model and perform table manipulations. Once our data model is ready we can start transforming our data by using various drag and drop nodes that contain different functions pertaining to the desired end result. Here I will be explaining some of the important SQL functions for manipulating columns.
First, we enter the modeling layer. Then, we select the “Column Manipulations”. Here we can see all the functions that can be used on the columns.
There are many cases where advanced functions and logical statements might be required in your models, and this needs a little bit of coding knowledge. We also have a course on Bigquery on Udemy, if you are interested in learning more about coding. For this reason, Y42 introduced the Functions node.
Using the Functions node, you can perform:
When a functions node is selected, it can be dragged and dropped into the canvas.It has to be connected to a valid input node in the canvas to be used, i.e the model should already have an imported data source in Y42, which we have covered in the previous post. Please check that out for a refresher.
Once a specific field is entered that needs to be transformed, it automatically shows the data type. It’s always a good practice to name your function node to understand which transformation is taking place as you can have multiple nodes throughout your model and it can get quite confusing.
Click on the Functions node to apply the settings. On the right side, you have access to the documentation for each available function
Here you can transform the columns as you wish. You can either create a new one or edit the same one.
As mentioned before, we are very fond of the F1 races. Every year during the F1 race, we send out a form to our team members who try to predict who would win the race. Along with that we even collect the top 5 players and the best team. The data is collected one day before the race and the points are calculated. So we have a track record of every race and every participant with points, this data has been integrated into Y42 and the previous blog shows you how we did that. The next step is understanding how to manipulate the data using data modeling which is also explained here.
To summarize, we explored the Y42’s UI modeling layer once more, focusing on the Functions node and explaining how to use it. We then implemented it in our own model about F1 races by concatenating the season and the round number to identify which driver won the race.
I will end this session here for now but in the upcoming posts, Sanu and I will dive deeper into other nodes and their functionalities which will help in transforming our data just like we want it. In the end, we will build our very own F1 2022 datadice dashboard and see who the winners are!