Cloud Scale Analytics with Azure Data Services: Build modern data warehouses on Microsoft Azure

Borosch, Patrik

  • 出版商: Packt Publishing
  • 出版日期: 2021-07-23
  • 售價: $2,180
  • 貴賓價: 9.5$2,071
  • 語言: 英文
  • 頁數: 520
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1800562934
  • ISBN-13: 9781800562936
  • 相關分類: Microsoft Azure
  • 海外代購書籍(需單獨結帳)
    無現貨庫存(No stock available)


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.


Patrik Borosch is a Cloud Solution Architect for Data and AI at Microsoft Switzerland GmbH. He has more than 25 years of BI and analytics development, engineering, and architecture experience and is a Microsoft Certified Data Engineer and a Microsoft Certified AI Engineer. Patrik has worked on numerous significant international Data Warehouse, Data Integration and Big Data projects. There, he has built and extended his experience in all facets from requirement engineering over data modelling and ETL all the way to reporting and dashboarding. At Microsoft Switzerland, he supports customers in their journey into the analytical world of Azure Cloud.


  1. Balancing the benefits of Data Lakes over Data Warehouses
  2. The Modern Data Warehouse and Azure Data Services
  3. Understanding the Data Lake Storage Layer
  4. Relational Storage components: Synapse SQL Pools, SQL DB, Azure Databases
  5. Data integration enterprise grade and even code-free
  6. Spark on Azure: Synapse Spark Pools
  7. Spark on Azure: Databricks
  8. Streaming
  9. Azure Cognitive Services / Azure Machine Learning
  10. Machine Learning with Spark on Azure: Synapse Spark Pools / Azure Databricks
  11. Synapse SQL Pools / Synapse Analytics
  12. Analysis Service / Power BI / Data Share
  13. Industry Data Models
  14. Data Governance