• 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 NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Data Administration