20776: Performing Big Data Engineering on Microsoft Cloud

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Course Description

About the Course

This course describes how to process Big Data using Azure tools and services including Azure Stream Analytics, Azure Data Lake, Azure SQL Data Warehouse and Azure Data Factory. The course also explains how to include custom functions, and integrate Python and R.


This course is based on the objectives of the course version 20776A.

Schedule & Fees

Instructor-Led Online Training
Location Fees Schedule
Client's Home/Office
Duration : 5 Days
Reserve a Seat
02 Mar - 06 Mar (Weekdays), 13 Apr - 17 Apr (Weekdays), 04 May - 08 May (Weekdays),
Delivery Mode : Classroom Training*
Location Fees Schedule
Duration : 5 Days
Reserve a Seat
$1550(Indian Price)
24 Feb - 28 Feb (Weekdays), 23 Mar - 27 Mar (Weekdays),
  • Weekends :
  • Sat, 04 Apr
  • Sun, 05 Apr
  • Sat, 11 Apr
  • Sun, 12 Apr
  • Sat, 18 Apr
Duration : 5 Days
Reserve a Seat
24 Feb - 28 Feb (Weekdays), 16 Mar - 20 Mar (Weekdays), 20 Apr - 24 Apr (Weekdays),
Client's Home/Office
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  • Describe common architectures for processing big data using Azure tools and services
  • Describe how to use Azure Stream Analytics to design and implement stream processing over large-scale data
  • Describe how to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job
  • Describe how to use Azure Data Lake Store as a large-scale repository of data files
  • Describe how to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store
  • Describe how to create and deploy custom functions and operations, integrate with Python and R, and protect and optimize jobs
  • Describe how to use Azure SQL Data Warehouse to create a repository that can support large-scale analytical processing over data at rest
  • Describe how to use Azure SQL Data Warehouse to perform analytical processing, how to maintain performance, and how to protect the data
  • Describe how to use Azure Data Factory to import, transform, and transfer data between repositories and services

Target Audience

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


  • Required: A basic knowledge of the Microsoft Windows operating system and its core functionality
  • Required: A good knowledge of relational databases
  • Required: A good understanding of Azure data services
  • Recommended: CCC Big Data Foundation (BDF)

Course Curriculum

Course # : 20776
Exam : Performing Big Data Engineering on Microsoft Cloud Services

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