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Azure Data Engineering Cookbook Design and Implement Batch and Streaming Analytics Using Azure Cloud Services.
Over 90 recipes to help you orchestrate modern ETL/ELT workflows and perform analytics using Azure services more easily Key Features Build highly efficient ETL pipelines using the Microsoft Azure Data services Create and execute real-time processing solutions using Azure Databricks, Azure Stream Ana...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2021.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Azure Data Engineering Cookbook |h [electronic resource] : |b Design and Implement Batch and Streaming Analytics Using Azure Cloud Services. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2021. | ||
| 300 | |a 1 online resource (455 p.) | ||
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| 520 | |a Over 90 recipes to help you orchestrate modern ETL/ELT workflows and perform analytics using Azure services more easily Key Features Build highly efficient ETL pipelines using the Microsoft Azure Data services Create and execute real-time processing solutions using Azure Databricks, Azure Stream Analytics, and Azure Data Explorer Design and execute batch processing solutions using Azure Data Factory Book DescriptionData engineering is one of the faster growing job areas as Data Engineers are the ones who ensure that the data is extracted, provisioned and the data is of the highest quality for data analysis. This book uses various Azure services to implement and maintain infrastructure to extract data from multiple sources, and then transform and load it for data analysis. It takes you through different techniques for performing big data engineering using Microsoft Azure Data services. It begins by showing you how Azure Blob storage can be used for storing large amounts of unstructured data and how to use it for orchestrating a data workflow. You'll then work with different Cosmos DB APIs and Azure SQL Database. Moving on, you'll discover how to provision an Azure Synapse database and find out how to ingest and analyze data in Azure Synapse. As you advance, you'll cover the design and implementation of batch processing solutions using Azure Data Factory, and understand how to manage, maintain, and secure Azure Data Factory pipelines. You’ll also design and implement batch processing solutions using Azure Databricks and then manage and secure Azure Databricks clusters and jobs. In the concluding chapters, you'll learn how to process streaming data using Azure Stream Analytics and Data Explorer. By the end of this Azure book, you'll have gained the knowledge you need to be able to orchestrate batch and real-time ETL workflows in Microsoft Azure. What you will learn Use Azure Blob storage for storing large amounts of unstructured data Perform CRUD operations on the Cosmos Table API Implement elastic pools and business continuity with Azure SQL Database Ingest and analyze data using Azure Synapse Analytics Develop Data Factory data flows to extract data from multiple sources Manage, maintain, and secure Azure Data Factory pipelines Process streaming data using Azure Stream Analytics and Data Explorer Who this book is for This book is for Data Engineers, Database administrators, Database developers, and extract, load, transform (ETL) developers looking to build expertise in Azure Data engineering using a recipe-based approach. Technical architects and database architects with experience in designing data or ETL applications either on-premise or on any other cloud vendor who wants to learn Azure Data engineering concepts will also find this book useful. Prior knowledge of Azure fundamentals and data engineering concepts is needed. | ||
| 505 | 0 | |a Table of Contents Working with Azure Blob Storage Working with Relational Database in Azure Analyzing Data with Azure Synapse Analytics Control Flow Activities in Azure Data Factory Control Flow Transformation and Copy Data Activity in Azure Data Factory Data Flow in Azure Data Factory Azure Data Factory Integration Runtime Deploying Azure Data Factory Pipelines Batch and Streaming Data Processing with Azure Databricks. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 630 | 0 | 0 | |a Microsoft Azure (Computing platform) |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Cloud computing. | |
| 650 | 0 | |a Streaming technology (Telecommunications) | |
| 650 | 0 | |a Electronic data processing |x Batch processing. | |
| 650 | 6 | |a Bases de données |x Gestion. | |
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| 650 | 7 | |a Electronic data processing |x Batch processing |2 fast | |
| 650 | 7 | |a Streaming technology (Telecommunications) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Osama, Ahmad |t Azure Data Engineering Cookbook |d Birmingham : Packt Publishing, Limited,c2021 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781800206557/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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