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

Summary

This chapter took us into the world of Databricks. You provisioned a Databricks workspace and examined it. In the workspace, you created a new Spark cluster and learned how to manage it.

You created a Databricks notebook, ran it interactively, and saw how to visualize data in your notebook. You also saw how to create a batch job from your notebook and learned about other alternatives for running code as a batch in your environment.

In the section that followed, you learned about Databricks tables and we examined additional capabilities, such as using Delta Lake to manage your data in your environment.

We saw how to add additional functionality using third-party libraries and how to create dashboards from your data.

Finally, we examined security features, such as access controls and secrets, and learned about networking features and how to integrate with Azure Monitor.

There are many more topics related to Azure Databricks that would have exceeded the capacity...