Cloud Scale Analytics mit Azure Data Services: Erstellen Sie moderne Data Warehouses...

by Borosch, Patrik | PB | LikeNew
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“Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, ...
Binding
Paperback
Book Title
Cloud Scale Analytics with Azure Data Services
Weight
1 lbs
Product Group
Book
IsTextBook
No
ISBN
9781800562936
Kategorie

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Product Identifiers

Publisher
Packt Publishing, The Limited
ISBN-10
1800562934
ISBN-13
9781800562936
eBay Product ID (ePID)
5050406273

Product Key Features

Publication Year
2021
Subject
Data Modeling & Design, General, Data Visualization, Data Processing
Number of Pages
520 Pages
Language
English
Publication Name
Cloud Scale Analytics with Azure Data Services : Build Modern Data Warehouses on Microsoft Azure
Type
Textbook
Author
Patrik Borosch
Subject Area
Mathematics, Computers
Format
Trade Paperback

Additional Product Features

Dewey Edition
23
Dewey Decimal
005.745
Synopsis
A practical guide to implementing a scalable and fast state-of-the-art analytical data estateKey Features* Store and analyze data with enterprise-grade security and auditing* Perform batch, streaming, and interactive analytics to optimize your big data solutions with ease* Develop and run parallel data processing programs using real-world enterprise scenariosBook DescriptionAzure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality.This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs.By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs.What you will learn* Implement data governance with Azure services* Use integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure Monitor* Explore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wrangling* Implement networking with Synapse Analytics and Spark pools* Create and run Spark jobs with Databricks clusters* Implement streaming using Azure Functions, a serverless runtime environment on Azure* Explore the predefined ML services in Azure and use them in your appWho this book is forThis book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required., A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key Features: Store and analyze data with enterprise-grade security and auditing Perform batch, streaming, and interactive analytics to optimize your big data solutions with ease Develop and run parallel data processing programs using real-world enterprise scenarios Book Description: Azure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs. What You Will Learn: Implement data governance with Azure services Use integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure Monitor Explore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wrangling Implement networking with Synapse Analytics and Spark pools Create and run Spark jobs with Databricks clusters Implement streaming using Azure Functions, a serverless runtime environment on Azure Explore the predefined ML services in Azure and use them in your app Who this book is for: This book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required.
LC Classification Number
QA76.9.D37

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