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

Implementing an ASA job

ASA offers you a convenient way to create streaming analysis on a configuration basis. This means you do not need to code the environment, the engine, the connection, logging, and so on. The service will take care of all these tasks for you (to see an example, refer to the Integrating sources and Writing to sinks sections that follow). The only thing you will need to code is the analytical core of your streaming job. To ease things for you, this is done using a SQL dialect that was tailored for this task (see Understanding ASA SQL).

After the provisioning of your new resource, you are taken to the following overview blade:

Figure 8.2 – Overview blade of the ASA job

You can already see three of the most important areas of your ASA job:

  • Inputs: This will show all the configured source connections available in your job.
  • Outputs: This will show all the configured target connections available in your job.
  • Query:...