Book Image

Data Lakehouse in Action

By : Pradeep Menon
5 (1)
Book Image

Data Lakehouse in Action

5 (1)
By: Pradeep Menon

Overview of this book

The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success. The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application. By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner.
Table of Contents (14 chapters)
1
PART 1: Architectural Patterns for Analytics
4
PART 2: Data Lakehouse Component Deep Dive
10
PART 3: Implementing and Governing a Data Lakehouse

Chapter 3: Ingesting and Processing Data in a Data Lakehouse

In the previous chapter, we provided an overview of the architectural components of a data lakehouse. That chapter provided a bird's-eye view of the seven layers and described these layers in considerable detail. This chapter will cover the architectural patterns for the first two layers of a data lakehouse:

  • The data ingestion layer
  • The data processing layer

These two layers need to be covered together as they are interlinked. Data is relayed from the ingestion layer to the processing layer. Many of the tools and technologies that are used in both these layers are the same.

This chapter is divided into five sections. We will start by exploring the differences between the extract, transform, load (ETL) and extract, load, transform (ELT) data transformation patterns. Then, we will dive deeper into the methods for ingesting and processing batch data. After that, we will do the same for streaming data...