Summary
In this chapter, we went through several scenarios that highlighted a couple of important points.
Firstly, the importance of data-driven analytics is the latest trend that will continue to grow in the future. Data-driven analytics gives decision makers the power to make key decisions but also to back these decisions up with valid reasons.
Secondly, data engineering is the backbone of all data analytics operations. None of the magic in data analytics could be performed without a well-designed, secure, scalable, highly available, and performance-tuned data repository—a data lake.
In the next few chapters, we will be talking about data lakes in depth. We will start by highlighting the building blocks of effective data—storage and compute. We will also look at some well-known architecture patterns that can help you create an effective data lake—one that effectively handles analytical requirements for varying use cases.
