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 additional libraries with your Spark pool

There are so many cases where you need to rely on additional functionality from third-party libraries. Synapse Spark supports the addition of libraries to your Spark pool and will make them available when the pool is instantiated. There are different options available for you to use this functionality.

Using public libraries

In the case of PyPi packages, you would create a file named requirements.txt and add it to the configuration of your Spark pool. Within this file, you can list all the packages that you want to include upon starting a Spark instance. The format for how you name the packages follows the pip freeze format and will include the package version next to the package name:

packagename==1.2.1

The requirements.txt file can be uploaded to the Packages section of the Spark pool properties during creation. You can do this later, too, if you need to.

You'll find the location to upload your file in Figure 6.16...