Data Engineering with Apache Spark, Delta Lake, and Lakehouse: Create scalable pipelines (True PDF, EPUB)

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English | 2021 | ISBN: 1801077746 | 480 pages | True PDF EPUB | 51.79 MB

Understand the complexities of modern-day data eeering platforms and explore strats to deal with them with the help of use case scenarios led by an industry expert in big data

Key Features

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

Book Description

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 eeering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data eeering. 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 strats 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 eeering book, you’ll know how to effectively deal with ever-chag data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks.

What is the Filename:Data Engineering with Apache Spark, Delta Lake, and Lakehouse: Create scalable pipelines (True PDF, EPUB)
The File is created by:Amazon
License:Public Domain
Edition:Digital edition
Original cover:Yes
Last revision:2021
Some Other details: N/A

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