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 Structured Streaming with Spark

If you are more the kind of developer that loves to code and you are a fan of Spark, maybe you want to have a look at Structured Streaming with Spark. This might be an interesting alternative for you.

Spark clusters are a widely used engine to implement streaming analytics using one of the available programming languages, such as Python or Scala. With the massive scalability of Spark clusters in Azure services such as Synapse or Databricks, you will be able to implement an environment that can grow with your needs and deliver the necessary performance.

Next to performance, there is the extensibility of Spark clusters that is a factor to consider. You will be able to combine streaming algorithms with the capabilities of Spark and programming languages such as Python (PySpark), Scala, or R.

Take Kafka as input for your streaming analysis, for example. Kafka is an event streaming platform that is quite widely used. ASA does not yet offer...