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

Looking for e-learning opportunities to learn Big Data engineering and enhance skills and knowledge of The Microsoft cloud service, then look no farther as you have reached the right place. At QuickStart a number of DataScience Trainings are offered to brush up your expertise and provide you with a career boost. the MS-20776 course focuses on the explanation and the process of including custom functions and integration of Python and R.
  • Virtual Classroom

    Learning Style
  • Intermediate

    Difficulty
  • 5 Days

    Course Duration
  • 5 Days

    SATV Value
  • Course Info

  • Sample Certificate

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Looking for e-learning opportunities to learn Big Data engineering and enhance skills and knowledge of The Microsoft cloud service, then look no farther as you have reached the right place. At QuickStart a number of DataScience Trainings are offered to brush up your expertise and provide you with a career boost. the MS-20776 course focuses on the explanation and the process of including custom functions and integration of Python and R.

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.

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.

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)

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More Information
BrandMicrosoft
Lab AccessYes
TechnologyMicrosoft
Learning StyleVirtual Classroom
DifficultyIntermediate
Course Duration5 Days
LanguageEnglish
SATV Value5 Days
VPA DiscountVPA Discount
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Will I get my certificate upon completion?

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