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

Adding data transformation logic

Up to now, we have examined the ELT mode of Synapse pipelines/Azure Data Factory. But like other data integration tools, Synapse pipelines and Data Factory offer the option to create logical dependencies in your pipeline run. You can put together different activities in a single pipeline and connect them to form your loading logic.

Figure 5.28 shows the possibilities for adding dependencies between two activities. In this way, you can set up complex loading logic. The displayed dependency would run the ForEach activity only when the Copy data activity has been run successfully:

Figure 5.25 – Adding dependencies to your pipeline

But what about transformation logic? What about the need to calculate, filter, or aggregate data and add other information from elsewhere to your data?

You can use data flows to do so. Data flows will give you a graphical interface to "program" your transformation logic and you...