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A Developer's Guide to Building Resilient Cloud Applications with Azure

You're reading from   A Developer's Guide to Building Resilient Cloud Applications with Azure Deploy applications on serverless and event-driven architecture using a cloud database

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781804611715
Length 296 pages
Edition 1st Edition
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Author (1):
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Hamida Rebai Hamida Rebai
Author Profile Icon Hamida Rebai
Hamida Rebai
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Table of Contents (18) Chapters Close

Preface 1. Part 1: Building Cloud-Oriented Apps Using Patterns and Technologies
2. Chapter 1: Introduction to Serverless Architecture, Event-Driven Architecture, and Cloud Databases FREE CHAPTER 3. Chapter 2: API Management – Import, Manage, and Publish Your First API 4. Chapter 3: Developing Event-Based and Message-Based Solutions 5. Part 2: Connecting Your Application with Azure Databases
6. Chapter 4: Creating and Deploying a Function App in Azure 7. Chapter 5: Develop an Azure Service Fabric Distributed Application 8. Chapter 6: Introduction to Application Data 9. Chapter 7: Working with Azure SQL Database 10. Chapter 8: Working with Azure Storage 11. Chapter 9: Working with Azure Cosmos DB to Manage Database Services 12. Chapter 10: Big Data Storage Overview 13. Part 3: Ensuring Continuous Integration and Continuous Container Deployment on Azure
14. Chapter 11: Containers and Continuous Deployment on Azure 15. Assessments 16. Index 17. Other Books You May Enjoy

Exploring Azure Data Lake Storage

Azure Storage includes five Azure services:

  • Azure Blob Storage, which is scalable storage for unstructured data
  • Azure Queue Storage, which allows us to build a reliable queue of messages
  • Azure Table Storage, which provides support for storing structured data
  • Azure Files Storage, which is used for cloud-based file shares
  • Azure Disks Storage, which provides managed storage for virtual machines disks

A data lake is a form of file storage, typically on a distributed filesystem for high-performance data access. The technologies that are commonly used to process queries against stored files and return data for reporting and analysis are Spark and Hadoop. These systems rely on a read-schema approach, which defines a tabular schema for semi-structured data files, where the data is parsed as it is read and no restrictions are applied when it is saved. Data lakes are ideal for supporting a mix of structured, semi-structured, and...

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