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

ETL

A very popular process known as ETL helps in building a target data source to house data that is consumable by applications. Generally, the data is in a raw format, and to make it consumable, the data should go through the following three distinct phases:

  • Extract: During this phase, data is extracted from multiple places. For instance, there could be multiple sources and they all need to be connected together in order to retrieve the data. Extract phases typically use data connectors consisting of connection information related to the target data source. They might also have temporary storage to bring the data from the data source and store it for faster retrieval. This phase is responsible for the ingestion of data.
  • Transform: The data that is available after the extract phase might not be directly consumable by applications. This could be for a variety of reasons; for example, the data might have irregularities, there might be missing data, or...