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

Organizing your data lake

A well-structured system of zones/layers and folders will help you control your data lake. On the one hand, you will find a canonical approach that makes it easier to understand structures and the semantics behind these zones and folders. On the other hand, generator approaches will enable you to automate processes in your modern data warehouse.

Many Big Data projects suffer from poorly organized folder structures, and it becomes a challenge to find the right data for the right analysis at the right time. The so-called data swamp can be nearly impossible to use and will even demotivate users from leveraging the effort that must be put into it.

Talking about zones in your data lake

In Chapter 1, Balancing the Benefits of Data Lakes over Data Warehouses, we addressed the question of zones in a data lake. We compared them to the layers in a data warehouse and found that they are pretty similar, and mostly follow similar semantics:

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