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

Bringing it all together

So far, we have covered the essential elements of batch and stream ingestion and processing. Now, let's bring these two types of processing together to define the Lambda architecture pattern.

Figure 3.12 – Lambda architecture pattern

The preceding diagram depicts a Lambda architecture pattern. A Lambda architecture pattern has three layers: the batch layer, the speed layer, and the serving layer.

The batch layer

The following diagram illustrates batch layer processing in a Lambda architecture:

Figure 3.13 – The batch layer in a Lambda architecture

Batch layer processing consists of ingesting the data into the raw data store of the data lake using pull or push methodologies through a batch data ingestion service. Once the data has been ingested in the raw data store, a batch processing service is initiated. The batch processing service employs a distributed computing engine for faster...