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

Using Azure Machine Learning with your modern data warehouse

Machine learning models can help you in many situations to improve business processes. Customer churn, fraud detection, and machine failure predictions are examples of where machine learning can support you in finding answers to tricky questions in a way that you would not, or only with excessive effort, be able to find otherwise.

However, a machine learning model that is not integrated into your daily business routine or one that will only be processed by a specialist on an on-demand basis will not perform with the full efficiency that might be possible.

One of the advantages of Synapse pipelines (and, of course, the Azure Data Factory standalone version as well) is the tight integration with other Azure services. Azure Machine Learning is one of them. Let's use our model from above and integrate it with an Azure pipeline. This will enable you to integrate Azure ML with all the data that you land in your data...