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Key benefits

Become well-versed with the core concepts of Apache Spark and Delta Lake for building data platforms

Learn how to ingest, process, and analyze data that can be later used for training machine learning models

Understand how to operationalize data models in production using curated data

Description

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.

Who is this book for?

This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.

What you will learn

Discover the challenges you may face in the data engineering world

Add ACID transactions to Apache Spark using Delta Lake

Understand effective design strategies to build enterprise-grade data lakes

Explore architectural and design patterns for building efficient data ingestion pipelines

Orchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIs

Automate deployment and monitoring of data pipelines in production

Get to grips with securing, monitoring, and managing data pipelines models efficiently

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

£36.99

£48.99

£38.99

Total £ 124.97

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Amazon verified reviews

John P Jan 15, 2022

I've worked tangential to these technologies for years, just never felt like I had time to get into it. This book, with it's casual writing style and succinct examples gave me a good understanding in a short time.The deployment guide and accompanying code made it easy to play along!

Amazon Verified review

Ravi S. Oct 23, 2021

The author has given abundance of In-depth knowledge. It also goes without saying that such kind of knowledge comes only with actual experience in the field and the willingness to share the knowledge with others like me who have just started in Big Data EngineeringIf there were more stars than five I would give it happily

RK Oct 24, 2021

I would highly recommend this book for those who are looking to deepen their knowledge in Data engineering! Coming from an Oracle background, I find this book is a good way to strengthen my understanding of how to utilize Microsoft Azure cloud services efficiently for data engineering. The book also illustrates examples with code snippets for better understanding and performance. You'll also discover how to deal efficiently with ever-changing data and design scalable data pipelines to expedite data science, machine learning (ML), and artificial intelligence (AI) processes. But what makes this book a must-read is the plethora of real-world examples based on actual scenarios. Five stars! ⭐️⭐️⭐️⭐️⭐️

VKumar Oct 23, 2021

A must read who is even remotely interested in data. Beautifully explained and a go-to guide not just for graduate students but also for professionals.

John J. Flavin Jan 14, 2022

I am encouraged to finally read a book that is equally useful for technical as well as non-technical folks. Not being a practitioner at the hands-on tech level, one can often get overloaded with the big data and data engineering jargon. I could easily relate to the first few chapters of the book and understand the evolution of big data. Among all chapters “Solving Data Engineering Challenges” stands out to me because it highlights the known challenges around data engineering while offering clear cut solutions. Obviously, the author has been part of several data engineering projects in the past enabling him to collect such practical examples. In the future I would like the author to write a similar book for the AWS Cloud.

Data Engineering with Apache Spark, Delta Lake, and Lakehouse: Create scalable pipelines that ingest, curate, and aggregate complex data in a timely and secure way

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