Summary

Congratulations on completing your first steps in learning about Azure Data Explorer! In this chapter, you learned about the different stages of the data analytics pipeline. Understanding the stages of the pipeline helps simplify your ability to comprehend the workflow of taking raw data and performing analysis on the data and visualizing your findings.

We then introduced some of the popular Azure data analytics services and mapped them to the different stages of the data analytics pipeline. Some of the services, such as Event Hubs, will be used in later chapters to ingest data into our own ADX databases.

We then learned what ADX is, what the main features are, and briefly looked at the ADX architecture to understand how ADX provides excellent performance by using both column stores and row stores, and how ADX scales both vertically and horizontally efficiently by implementing one of the fundamental Azure design principles of decoupling compute and storage. We then discussed some of the use cases of ADX that we will use throughout this book, such as time series analysis.

Finally, we learned how to connect to ADX clusters and query databases using the ADX UI. In the next chapter, we will learn how to create and manage our own ADX clusters and databases using the Azure portal, PowerShell, and the Azure CLI.

Before moving on to the next chapter, try modifying ${HOME}/Scalable-Data-Analytics-with-Azure-Data-Explorer/Chapter01/first-query.kql and display an area chart. The solution can be found at ${HOME}/Scalable-Data-Analytics-with-Azure-Data-Explorer/Chapter01/population-areachart.kql. What other types of charts can you render?

Additionally, here is some information you should know. The Azure Data Explorer UI supports a feature known as IntelliSense, as shown in Figure 1.11. IntelliSense provides code completion and hints when you are writing your queries, so you do not need to worry about memorizing all the keywords:

Figure 1.11 – IntelliSense features

We will be using IntelliSense throughout this book when using both Visual Studio Code and the Azure Data Explorer Web UI. Visual Studio Code will be used for editing our scripts and ARM templates, and the Azure Data Explorer Web UI is where we will execute most of our KQL queries.