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Data Engineering with Apache Spark, Delta Lake, and Lakehouse

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

By : Kukreja
4.7 (58)
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Data Engineering with Apache Spark, Delta Lake, and Lakehouse

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

4.7 (58)
By: Kukreja

Overview of this book

In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks.
Table of Contents (17 chapters)
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1
Section 1: Modern Data Engineering and Tools
5
Section 2: Data Pipelines and Stages of Data Engineering
11
Section 3: Data Engineering Challenges and Effective Deployment Strategies

Introducing data engineering in Azure

In recent years, Microsoft Azure has added several powerful services to its arsenal that seamlessly collect, store, process, and publish data for both batch and streaming workloads. Gone are the days where choices for storage and compute were severely limited among cloud vendors. As a user, you simply needed to conform with the supplied tools and services: now, your options are more extensive.

Today, the cloud ecosystem looks very different from what it did previously. The growth of cloud services allows users to choose from a variety of storage, compute, and deployment options. As an example, if I want to run a Spark program, I can choose from at least four different options in Microsoft Azure. The real question is, if all four options are running Apache Spark, then why are these options even required?

Important Note

The array of options available on the cloud are not limited to compute only: the same variety exists for data collection...

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