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