Book Image

Cloud Scale Analytics with Azure Data Services

By : Patrik Borosch
Book Image

Cloud Scale Analytics with Azure Data Services

By: Patrik Borosch

Overview of this book

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.
Table of Contents (20 chapters)
1
Section 1: Data Warehousing and Considerations Regarding Cloud Computing
4
Section 2: The Storage Layer
7
Section 3: Cloud-Scale Data Integration and Data Transformation
14
Section 4: Data Presentation, Dashboarding, and Distribution

Questions

The following are additional questions from the Asking in the right direction section:

  • General questions: Your modern data warehouse may need to hold data for a longer period. Do you need different access tiers (hot, cool, or archive)? Is older data not accessed that often? How do you need to design the access rights to the data? Are there only automatic processes, or will users want to access data themselves? Do you need to establish replication for reliability, and to what extent?
  • Data loading: Are you planning for a new ETL/ELT tool? Do you want to run that in the cloud or on-premises? What are your expectations for the availability of connectors? What are your expectations for usability, or do you want to code your data transport layer? What language do you prefer for this? What will the volume of the data be that is to be transported? Do you need parallel processes? Do you expect scalability?
  • Data transformation: Do you want to perform data transformation...