Performing Big Data Engineering on Microsoft Cloud Services (MS-20776)

QuickStart
Training overview
2 hours
Professional Course

Course description

Performing Big Data Engineering on Microsoft Cloud Services (MS-20776)

About this course:

Performing big data engineering is a 5 day, intermediate difficulty level course. The courseware is carefully designed by professionals to deliver precise and updated information. The course sheds light on the processing of Big Data using Azure tools and services including Azure stream analytics, Azure Data Lake, SQL Data Warehouse and Data Factory. Additionally it describes how to include custom functions, and integrate Python and R.

A Data Professional with Microsoft Azure skills earns around $111,181 annually.

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Who should attend?

Audience:

The target audience of this course is data engineers (IT professionals, developers, and information workers) who plan to implement big data engineering workflows on Azure.

Prerequisite:

In addition to their professional experience, students who attend this training should already have the following technical knowledge: 

  • Good understanding of Azure data services
  • Basic knowledge of the Microsoft Windows operating system and its core functionality
  • Good knowledge of relational databases

Suggested prerequisites courses:

  • Azure Fundamentals
  • Administering Microsoft SQL Server Databases (MS-20462)

Training content

Course Objective:

On successful completion of the course, students will be able to:

  • Understand and explain the common architectures for processing big data using Azure tools and services.
  • Understand and explain the use of Azure Stream Analytics to design and implement stream processing over large-scale data.
  • Understand and explain the  process of including custom functions and incorporating machine learning activities into an Azure Stream Analytics job.
  • Understand and explain the use of Azure Data Lake Store as a large-scale repository of data files.
  • Understand and explain the use of Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store.
  • Understand and explain the process of creating and implementing customised functions and operations, and to combine with Python and R.
  •  Protect and optimize jobs.
  • Understand and explain the use of Azure SQL Data Warehouse to build a repository that can support large-scale analytical processing over data at rest.
  • Understand and explain the use of Azure SQL Data Warehouse to perform analytical processing, sustain performance, and protect the data.
  • Understand and explain the use of Azure Data Factory to import, modify, and transfer data between repositories and services.

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About QuickStart

Meet your career goals with QuickStart!

QuickStart exists to create world-class technologists by personalizing and individualizing training to address the massive skills gap in the IT industry. Through 20 years of research and data analysis, we’ve learned that a modern learner prefers to learn through multiple...


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