Summary
In this chapter, we focused on understanding how to use Alteryx for data engineering. In the examples, we looked at how a workflow can take data from the source to the end users. We also examined how we can make those datasets more usable for both the end user who requested the data and any other user who may require that data in the future.
We also introduced DataOps and explained how the framework makes the data pipeline process automated and faster while delivering higher quality data.
In the next chapter, we will look at DataOps in more depth. We will learn how to apply DevOps with Alteryx and detail the benefits that a DevOps practice will bring to your Alteryx data engineering.
