Learning Azure Stream Analytics

Learning Azure Stream Analytics

28mGeneral2024-02-23

Authors

Karsten Ulferts

Karsten Ulferts

Helping companies integrate cloud computing into their IT strategies

Course details

Data has the power to transform how we live by revealing meaningful insights that we never knew were there. In this course, designed uniquely for Azure developers and data scientists, join instructor Karsten Ulferts as he provides a comprehensive introduction to the fundamental concepts, capabilities, and practical applications of Azure Stream Analytics.

Learn how to set up your Azure Stream Analytics environment, work with data sources and sinks, perform basic operations in the Stream Analytics Query Language (SAQL), and deploy resources efficiently at scale. By the end of this course, you’ll be equipped with the knowledge and skills required to leverage Azure Stream Analytics effectively for real-time data processing and analytics.

Skills covered

Data EngineeringAzureData AnalysisNetwork AdministrationCloud PlatformsLearningNetwork and System AdministrationCloud ComputingData ScienceBusiness Analysis and StrategyBusiness Software and ToolsMicrosoft

Concepts

Introduction

  • Data transforms the way people live
  • What you should know

What Is Azure Stream Analytics

  • What is Azure Stream Analytics
  • Use cases and benefits of Azure Stream Analytics
  • Key concepts and components

Setting Up Azure Stream Analytics Environment

  • Creating an Azure account and subscription
  • Provisioning necessary resources
  • Configuring and managing Azure Stream Analytics jobs

Working with Data Sources and Sinks

  • Supported data formats and protocols
  • Integrating with various data storage and processing services

Stream Analytics Query Language (SAQL)

  • Overview of SAQL syntax and structure
  • Time-based and windowed operations
  • Joins and partitions in SAQL

Real-Time Analytics with Azure Stream Analytics

  • Scenario overview
  • Resource deployment
  • Generate sales transaction data
  • Query description
  • Scenario in action and rebuild

Scaling and Performance Optimization

  • Understanding scalability considerations
  • Partitioning and parallelism in Azure Stream Analytics

Conclusion

  • Next steps