Chapter 1: Introducing the Evolution of Data Analytics Patterns
Data analytics is an ever-changing field. A little history will help you appreciate the strides in this field and how data architectural patterns have evolved to fulfill the ever-changing need for analytics.
First, let's start with some definitions:
- What is analytics? Analytics is defined as any action that converts data into insights.
- What is data architecture? Data architecture is the structure that enables the storage, transformation, exploitation, and governance of data.
Analytics and the data architecture that enables analytics goes a long way. Let's now explore some of the patterns that have evolved over the last few decades.
This chapter explores the genesis of data growth and explains the need for a new paradigm in data architecture. This chapter starts by examining the predominant paradigm, the enterprise data warehouse, popular in the 1990s and 2000s. It explores the challenges associated with this paradigm and then covers the drivers that caused an explosion in data. It further examines the rise of a new paradigm, the data lake, and its challenges. Furthermore, this chapter ends by advocating the need for a new paradigm, the data lakehouse. It clarifies the key benefits delivered by a well-architected data lakehouse.
We'll cover all of this in the following topics:
- Discovering the enterprise data warehouse era
- Exploring the five factors of change
- Investigating the data lake era
- Introducing the data lakehouse paradigm
