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



Data Engineering with Apache Spark, Delta Lake, and Lakehouse: Create scalable pipelines (True PDF, EPUB)
English | 2021 | ISBN: 1801077746 | 480 pages | True PDF EPUB | 51.79 MB

In the world of ever-chag data and schemas, it is important to build data pipelines that can auto-adjust to changes.


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 you will learn

Discover the challenges you may face in the data eeering world

Add ACID transactions to Apache Spark using Delta Lake

Understand effective design strats 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

Who this book is for

This book is for aspiring data eeers and data analysts who are new to the world of data eeering 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 eeering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.

Table of Contents

The Story of Data Eeering and Analytics

Discovering Storage and Compute Data Lake Architectures

Data Eeering on Microsoft Azure

Understanding Data Pipelines

Data Collection Stage - The Bronze Layer

Understanding Delta Lake

Data Curation Stage - The Silver Layer

Data Aggregation Stage - The Gold Layer

Deploying and Monitoring Pipelines in Production

Solving Data Eeering Challenges

Infrastructure Provisioning

Continuous Integration and Deployment (D) of Data Pipelines



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