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Azure for Architects

You're reading from   Azure for Architects Create secure, scalable, high-availability applications on the cloud

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839215865
Length 698 pages
Edition 3rd Edition
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Authors (3):
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Jack Lee Jack Lee
Author Profile Icon Jack Lee
Jack Lee
Ritesh Modi Ritesh Modi
Author Profile Icon Ritesh Modi
Ritesh Modi
Rithin Skaria Rithin Skaria
Author Profile Icon Rithin Skaria
Rithin Skaria
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Toc

Table of Contents (21) Chapters Close

Preface 1. Getting started with Azure 2. Azure solution availability, scalability, and monitoring FREE CHAPTER 3. Design pattern – Networks, storage, messaging, and events 4. Automating architecture on Azure 5. Designing policies, locks, and tags for Azure deployments 6. Cost management for Azure solutions 7. Azure OLTP solutions 8. Architecting secure applications on Azure 9. Azure Big Data solutions 10. Serverless in Azure – Working with Azure Functions 11. Azure solutions using Azure Logic Apps, Event Grid, and Functions 12. Azure Big Data eventing solutions 13. Integrating Azure DevOps 14. Architecting Azure Kubernetes solutions 15. Cross-subscription deployments using ARM templates 16. ARM template modular design and implementation 17. Designing IoT solutions 18. Azure Synapse Analytics for architects 19. Architecting intelligent solutions 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...
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