Professional Course

Performing Data Engineering on Microsoft HD Insight (MS-20775)

QuickStart, Online
6 hours
6 hours
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Course description

Performing Data Engineering on Microsoft HD Insight (MS-20775)

About this course:

Microsoft Azure HDInsight provides a cost-effective, pay-as-you-go solution for Hadoop-based big data batch processing. It does not require the commitment of installment and configuration of infrastructure on the premises. The central aim of this course is to provide learners with the skills necessary to design and enact large data workflows on HDInsight. This is also a preparatory course for the Microsoft 70-775: Perform Data Engineering on Microsoft Azure HDInsight certification exam.

The salary for a Data Engineer averages $84,143 per annum.

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Who should attend?


The audiences for this course are intended to be:

  • Data engineers
  • Data architects
  • Data scientists
  • Data developers planning to implement large data engineering workflows on HDInsight


In addition to professional experience, prospective learners for this course are required to have the following knowledge and skills:

  • 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.

Suggested prerequisites courses:

  • Databases in Azure
  • SQL: Fundamentals of Querying (SQL-QueryFund)

Training content

Course Objective: 

Upon the completion of this course, students will be able to: 

  • Deploy HDInsight Clusters
  • Build Solutions that use Kafka and HBase
  • Authorize Users to Access Resources
  • Implement Batch Solutions
  • Loading Data into HDInsight.
  • Design Batch ETL Solutions for Big Data with Spark
  • Analyze Data with Spark SQL, Hive, and Phoenix
  • Troubleshooting HDInsight
  • Describe Stream Analytics
  • Develop Big Data Real-Time Processing Solutions with Apache Storm
  • Implement Spark Streaming Using the DStream API

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