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M20775: Performing Data Engineering on Microsoft HD Insight

5 Day Course
Hands On
Official Microsoft Curriculum
Code M20775

This course has been retired. Please view currently available Microsoft SQL Server Training Courses.

Modules

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Getting Started with HDInsight (6 topics)

  • What is Big Data?
  • Introduction to Hadoop
  • Working with MapReduce Function
  • Introducing HDInsight
  • Lab: Working with HDInsight
  • Provision an HDInsight cluster and run MapReduce jobs

Deploying HDInsight Clusters (7 topics)

  • Identifying HDInsight cluster types
  • Managing HDInsight clusters by using the Azure portal
  • Managing HDInsight Clusters by using Azure PowerShell
  • Lab: Managing HDInsight clusters with the Azure Portal
  • Create an HDInsight cluster that uses Data Lake Store storage
  • Customize HDInsight by using script actions
  • Delete an HDInsight cluster

Authorizing Users to Access Resources (6 topics)

  • Non-domain Joined clusters
  • Configuring domain-joined HDInsight clusters
  • Manage domain-joined HDInsight clusters
  • Lab: Authorizing Users to Access Resources
  • Prepare the Lab Environment
  • Manage a non-domain joined cluster

Loading data into HDInsight (5 topics)

  • Storing data for HDInsight processing
  • Using data loading tools
  • Maximising value from stored data
  • Lab: Loading Data into your Azure account
  • Load data for use with HDInsight

Troubleshooting HDInsight (8 topics)

  • Analyze HDInsight logs
  • YARN logs
  • Heap dumps
  • Operations management suite
  • Lab: Troubleshooting HDInsight
  • Analyze HDInsight logs
  • Analyze YARN logs
  • Monitor resources with Operations Management Suite

Implementing Batch Solutions (7 topics)

  • Apache Hive storage
  • HDInsight data queries using Hive and Pig
  • Operationalize HDInsight
  • Lab: Implement Batch Solutions
  • Deploy HDInsight cluster and data storage
  • Use data transfers with HDInsight clusters
  • Query HDInsight cluster data

Design Batch ETL solutions for big data with Spark (8 topics)

  • What is Spark?
  • ETL with Spark
  • Spark performance
  • Lab: Design Batch ETL solutions for big data with Spark.
  • Create a HDInsight Cluster with access to Data Lake Store
  • Use HDInsight Spark cluster to analyze data in Data Lake Store
  • Analyzing website logs using a custom library with Apache Spark cluster on HDInsight
  • Managing resources for Apache Spark cluster on Azure HDInsight

Analyze Data with Spark SQL (6 topics)

  • Implementing iterative and interactive queries
  • Perform exploratory data analysis
  • Lab: Performing exploratory data analysis by using iterative and interactive queries
  • Build a machine learning application
  • Use zeppelin for interactive data analysis
  • View and manage Spark sessions by using Livy

Analyze Data with Hive and Phoenix (7 topics)

  • Implement interactive queries for big data with interactive hive.
  • Perform exploratory data analysis by using Hive
  • Perform interactive processing by using Apache Phoenix
  • Lab: Analyze data with Hive and Phoenix
  • Implement interactive queries for big data with interactive Hive
  • Perform exploratory data analysis by using Hive
  • Perform interactive processing by using Apache Phoenix

Stream Analytics (6 topics)

  • Stream analytics
  • Process streaming data from stream analytics
  • Managing stream analytics jobs
  • Lab: Implement Stream Analytics
  • Process streaming data with stream analytics
  • Managing stream analytics jobs

Implementing Streaming Solutions with Kafka and HBase (11 topics)

  • Building and Deploying a Kafka Cluster
  • Publishing, Consuming, and Processing data using the Kafka Cluster
  • Using HBase to store and Query Data
  • Lab: Implementing Streaming Solutions with Kafka and HBase
  • Create a virtual network and gateway
  • Create a storm cluster for Kafka
  • Create a Kafka producer
  • Create a streaming processor client topology
  • Create a Power BI dashboard and streaming dataset
  • Create an HBase cluster
  • Create a streaming processor to write to HBase

Develop big data real-time processing solutions with Apache Storm (7 topics)

  • Persist long term data
  • Stream data with Storm
  • Create Storm topologies
  • Configure Apache Storm
  • Lab: Developing big data real-time processing solutions with Apache Storm
  • Stream data with Storm
  • Create Storm Topologies

Create Spark Streaming Applications (7 topics)

  • Working with Spark Streaming
  • Creating Spark Structured Streaming Applications
  • Persistence and Visualization
  • Lab: Building a Spark Streaming Application
  • Installing Required Software
  • Building the Azure Infrastructure
  • Building a Spark Streaming Pipeline

Prerequisites

In addition to their professional experience, students who attend this course should have:

  • Programming experience using R, and familiarity with common R packages
  • Knowledge of common statistical methods and data analysis best practices.
  • Basic knowledge of the Microsoft Windows operating system and its core functionality.
  • Working knowledge of relational databases.
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