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Cloud Scale Analytics with Azure Data Services
Cloud Scale Analytics with Azure Data Services

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

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Cloud Scale Analytics with Azure Data Services

Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses

Is the Data Warehouse dead with the advent of Data Lakes? There is disagreement everywhere about the need for Data Warehousing in a modern data estate. With the rise of Data Lakes and Big Data technology, many people use other, newer technologies compared to databases for their analytical efforts. Establishing a data-driven company seems to be possible without all those narrow definitions and planned structures, the ETL/ELT, and all the indexing for performance. But when we examine the technology carefully, when we compare the requirements that are formulated in analytical projects, free of prejudice to the functionality that the chosen services or software packages can deliver, we often find gaps on both ends. This chapter discusses the capabilities of Data Warehousing and Data Lakes and introduces the concept of the Modern Data Warehouse.

With all the innovations that have been brought to us in the last few years, such as faster hardware, new technologies, and new dogmas such as the Data Lake, older concepts and methods are being questioned and challenged. In this chapter, I would like to explore the evolution of the analytical world and try to answer the question, is the Data Warehouse really obsolete?

We'll find out by covering the following topics:

  • Distinguishing between Data Warehouses and Data Lakes
  • Understanding the opportunities of modern cloud computing
  • Exploring the benefits of AI and ML
  • Answering the question
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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 23, 2021
Length: 520 pages
Edition : 1st
Language : English
ISBN-13 : 9781800562936
Category :
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Product Details

Publication date : Jul 23, 2021
Length: 520 pages
Edition : 1st
Language : English
ISBN-13 : 9781800562936
Category :
Concepts :
Tools :

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Frequently bought together


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Table of Contents

19 Chapters
Section 1: Data Warehousing and Considerations Regarding Cloud Computing Chevron down icon Chevron up icon
Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses Chevron down icon Chevron up icon
Chapter 2: Connecting Requirements and Technology Chevron down icon Chevron up icon
Section 2: The Storage Layer Chevron down icon Chevron up icon
Chapter 3: Understanding the Data Lake Storage Layer Chevron down icon Chevron up icon
Chapter 4: Understanding Synapse SQL Pools and SQL Options Chevron down icon Chevron up icon
Section 3: Cloud-Scale Data Integration and Data Transformation Chevron down icon Chevron up icon
Chapter 5: Integrating Data into Your Modern Data Warehouse Chevron down icon Chevron up icon
Chapter 6: Using Synapse Spark Pools Chevron down icon Chevron up icon
Chapter 7: Using Databricks Spark Clusters Chevron down icon Chevron up icon
Chapter 8: Streaming Data into Your MDWH Chevron down icon Chevron up icon
Chapter 9: Integrating Azure Cognitive Services and Machine Learning Chevron down icon Chevron up icon
Chapter 10: Loading the Presentation Layer Chevron down icon Chevron up icon
Section 4: Data Presentation, Dashboarding, and Distribution Chevron down icon Chevron up icon
Chapter 11: Developing and Maintaining the Presentation Layer Chevron down icon Chevron up icon
Chapter 12: Distributing Data Chevron down icon Chevron up icon
Chapter 13: Introducing Industry Data Models Chevron down icon Chevron up icon
Chapter 14: Establishing Data Governance Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(7 Ratings)
5 star 85.7%
4 star 14.3%
3 star 0%
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1 star 0%
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Bandar Alsharfi Jul 27, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I really enjoyed reading the book. It was simple and straight to the point. Hybrid data analytics platforms can be complex. The book takes you through a flow that explains how data ingestion works, and how data transformation tools can be leveraged in a cost efficient way. Cloud operating expenses can be high if the wrong services have been used. Patrik explains the purposes of these services with clear examples. I highly recommend this book to any level, not just beginners if you are looking at de-cluttering your mind about data analytics and hit refresh on hybrid data concepts.!!
Amazon Verified review Amazon
SWR Jul 26, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Full Disclosure: I am a Microsoft employee and I do know Patrik.That being said, I know the Azure analytics ecosystem very well and have read through his book with a critical eye. I've found his treatment of the subject to be comprehensive (in breadth) covering structured data, unstructured data, data lake, data warehouse, real time streaming, batch data processing, presentation layer, etc.Overall he has done a great job of explaining cloud scale analytics in Azure for those new to the subject, which can be a very complicated due to the plethora of technologies. The uninitiated will do well to have a proper guide and this book can be that guide.It should be noted that any of the areas covered may need supplemental depth materials as this book is more broad than deep. Aside from that, Patrik has put together an extremely helpful set of materials that would benefit anyone working in the analytics space on Azure.
Amazon Verified review Amazon
AdaptableTerran Nov 05, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
2021 was a great year for Azure data engineering books and "Cloud Scale Analytics" was one of my favorites. This book provides broad and practical coverage of the Azure data engineering toolbox. I’m grateful Mr. Borosch shared his knowledge and wrote an excellent book, one that I reference time and time again on my projects. If you’re a SQL BI developer looking to learn Azure data engineering, this is the book to get.
Amazon Verified review Amazon
Vignesh Balasubramanian Sep 23, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I received this book from a Packt representative. As a practitioner of data management and analytics, I found this book to be comprehensive for anyone looking to implement their data and analytics platform on Azure. What I like about this book is that it is organized into logical sections and depending on your data journey and familiarity with Azure products you can dive right into the section of interest. I would highly recommend this book for anyone looking to implement Synapse as their data warehouse and use related Azure services for data preparation (ELT/ETL), Dashboard and reporting and advanced analytics.
Amazon Verified review Amazon
Ralph Kemperdick Jul 25, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is the first time I have found a book which holistically covers all the aspects in a single book which are important to know about, when building to a cloud scale analytical solution.As it refers most of the time to the Azure platform, the author also pointed out which other technology pieces and solutions may apply when going on this journey.For me this book feels like a brain dump of one of the most recognized Microsoft Cloud Solution Architects in Europe.The number of possibilities with Azure Synapse and the associated services is described very well with a lot of details how to get things up and running. Specifically, the integration of SQL Pools and Apache Spark and the Azure Cognitive Service is a highlight, as it describes how to enrich your data with predictive intelligence.I love the aspects of many new or not even released technologies being pointed out in the book, like the Industry Data Workbench and the Data Governance with Azure Purview. The integration between Purview and Azure Synapse Workbench is explained on real usage scenarios and benefits for the data engineer.The concept of Power BI enabled self-service data marts is explained how to do it from a practical point of view.The book is targeted more at practitioners than on scientist who want to learn about the theoretical background. But it gives some very useful examples how to organize the Data Lake to make sure not ending up with a data swamp, which is a common pitfall.After having read the book, I wanted to learn more about the new services introduced in the book. I can imagine there is plenty of content out there which might be good for a second edition of this book.Overall a must read for people getting started with Azure and Azure Synapse Analytics in particular.
Amazon Verified review Amazon
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