• Online, Self-Paced
Course Description

Real-time big data analytics with Microsoft Azure is used in numerous critical applications that require immediate analysis and action. This course covers data ingesting and designing compute resources for real-time processing.

Learning Objectives

Ingesting Data for Real-time Analytics

  • start the course
  • identify some common use cases for real-time analytics
  • recognize the Apache NiFi capabilities for real-time big data analytics
  • identify the Apache NiFi architecture and performance characteristics
  • list key features of Apache NiFi
  • recognize how Azure Event Hubs capabilities are used for real-time big data analytics
  • list key features of Azure Event Hubs
  • create an Event Hub using the Azure portal
  • send messages to Azure Event Hubs in .NET Standard
  • receive messages with the event processor host in .NET Standard
  • receive events from Event Hubs using Apache Storm
  • enable Event Hubs capture using the Azure portal
  • recognize aspects of row key design in HBase
  • recognize various data partitioning schemes
  • design partitions for scalability, query performance, and availability

Compute Resources for Real-time Analytics

  • describe fundamental architectural concepts of enterprise analytics
  • recognize key components for real-time event processing
  • create Linux-based clusters in HDInsight using Azure PowerShell
  • manage Hadoop clusters in HDInsight using Azure PowerShell
  • list main phases in setting up a storm cluster

Practice: Real-time Processing Technologies

  • use data ingesting tools for real-time analytics

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.