Book Image

Azure for Architects - Third Edition

By : Ritesh Modi, Jack Lee, Rithin Skaria
Book Image

Azure for Architects - Third Edition

By: Ritesh Modi, Jack Lee, Rithin Skaria

Overview of this book

Thanks to its support for high availability, scalability, security, performance, and disaster recovery, Azure has been widely adopted to create and deploy different types of application with ease. Updated for the latest developments, this third edition of Azure for Architects helps you get to grips with the core concepts of designing serverless architecture, including containers, Kubernetes deployments, and big data solutions. You'll learn how to architect solutions such as serverless functions, you'll discover deployment patterns for containers and Kubernetes, and you'll explore large-scale big data processing using Spark and Databricks. As you advance, you'll implement DevOps using Azure DevOps, work with intelligent solutions using Azure Cognitive Services, and integrate security, high availability, and scalability into each solution. Finally, you'll delve into Azure security concepts such as OAuth, OpenConnect, and managed identities. By the end of this book, you'll have gained the confidence to design intelligent Azure solutions based on containers and serverless functions.
Table of Contents (21 chapters)
20
Index

A primer on Azure Data Factory

Azure Data Factory is a fully managed, highly available, highly scalable, and easy-to-use tool for creating integration solutions and implementing ETL phases. Data Factory helps you to create new pipelines in a drag and drop fashion using a user interface, without writing any code; however, it still provides features to allow you to write code in your preferred language.

There are a few important concepts to learn about before using the Data Factory service, which we will be exploring in more detail in the following sections:

  • Activities: Activities are individual tasks that enable the running and processing of logic within a Data Factory pipeline. There are multiple types of activities. There are activities related to data movement, data transformation, and control activities. Each activity has a policy through which it can decide the retry mechanism and retry interval.
  • Pipelines: Pipelines in Data Factory are composed...