You're reading from Azure Data Engineering Cookbook Get well versed in various data engineering techniques in Azure using this recipe-based guide

Product type Paperback

Published in Sep 2022

Publisher Packt

ISBN-13 9781803246789

Length 608 pages

Edition 2nd Edition

Languages

SQL

Tools

Azure

Concepts

Data Engineering

Authors (3):

Ahmad Osama

Nagaraj Venkatesan

Luca Zanna

View More author details

Table of Contents (16) Chapters

Preface

1. Chapter 1: Creating and Managing Data in Azure Data Lake

2. Chapter 2: Securing and Monitoring Data in Azure Data Lake FREE CHAPTER

3. Chapter 3: Building Data Ingestion Pipelines Using Azure Data Factory

4. Chapter 4: Azure Data Factory Integration Runtime

5. Chapter 5: Configuring and Securing Azure SQL Database

6. Chapter 6: Implementing High Availability and Monitoring in Azure SQL Database

7. Chapter 7: Processing Data Using Azure Databricks

8. Chapter 8: Processing Data Using Azure Synapse Analytics

9. Chapter 9: Transforming Data Using Azure Synapse Dataflows

10. Chapter 10: Building the Serving Layer in Azure Synapse SQL Pool

11. Chapter 11: Monitoring Synapse SQL and Spark Pools

12. Chapter 12: Optimizing and Maintaining Synapse SQL and Spark Pools

13. Chapter 13: Monitoring and Maintaining Azure Data Engineering Pipelines

14. Index

Why subscribe?

15. Other Books You May Enjoy

Creating and Managing Data in Azure Data Lake

Azure Data Lake is a highly scalable and durable object-based cloud storage solution from Microsoft. It is optimized to store large amounts of structured and semi-structured data such as logs, application data, and documents.

Azure Data Lake can be used as a data source and destination in data engineering projects. As a source, it can be used to stage structured or semi-structured data. As a destination, it can be used to store the result of a data pipeline.

Azure Data Lake is provisioned as a storage account in Azure, capable of storing files (blobs), tables, or queues. This book will focus on Azure Data Lake storage accounts used for storing blobs/files

In this chapter, we will learn how to provision, manage, and upload data into Data Lake accounts and will cover the following recipes: