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  • Book Overview & Buying Data Lakehouse in Action
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Data Lakehouse in Action

Data Lakehouse in Action

By : Pradeep Menon
4.1 (9)
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Data Lakehouse in Action

Data Lakehouse in Action

4.1 (9)
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)
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1
PART 1: Architectural Patterns for Analytics
4
PART 2: Data Lakehouse Component Deep Dive
10
PART 3: Implementing and Governing a Data Lakehouse

Ingesting and processing batch data

Let's start by looking at the logical architecture of a data lakehouse:

Figure 3.1 – Data lakehouse logical architecture

The preceding diagram depicts the seven logical layers. Data from the data providers needs to be ingested and transformed. Traditionally, there are two types of batch data ingestion and transformation patterns:

  • ETL
  • ELT

Understanding these patterns is vital if you wish to understand how they can be combined for batch ingestion and processing in a data lakehouse.

Let's discuss these patterns in detail.

Differences between the ETL and ELT patterns

Let's discuss the differences between these patterns in detail. On the surface, these patterns may seem similar. However, there are differences in their philosophy and the services that are employed to transform data.

ETL

The first pattern is ETL. The following diagram depicts a typical ETL pattern:

...
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Data Lakehouse in Action
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