20776A: Performing Big Data Engineering on Microsoft Cloud Services Training & Certification Courses
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Overview

This five-day 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 prepares you for Exam 70-776. Download course contents.

Need more info ? Email info@koenig-solutions.com  or   Enquire now!

Schedule & Prices

Delivery Mode Location Course Duration Fees Schedule
Instructor-Led Online Training (1-on-1) Client's Home/Office5 Days $ 2,330 As per mutual convenience (4-Hours Evenings & Weekends Possible
Classroom Training * Dubai 5 Days $ 2,600
4-8 Aug 2019
Delhi, Bangalore, Dehradun (Rishikesh), Goa, Shimla, Chennai 5 Days $ 1,785
22 Jul -26 Jul 2019,
29 Jul -2 Aug 2019,
5 Aug -9 Aug 2019,
2 Sep -6 Sep 2019,
9 Sep -13 Sep 2019,
7 Oct -11 Oct 2019
Fly-Me-a-Trainer Client's Location5 Days On Request As per mutual convenience

Course Prerequisites

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

  • A good understanding of Azure data services.
  • A basic knowledge of the Microsoft Windows operating system and its core functionality.
  • A good knowledge of relational databases.

FREE Trial Class FREE pre-requisite Class FREE revision Class

Need more info ? Email info@koenig-solutions.com  or   Live Chat

20776A: Performing Big Data Engineering on Microsoft Cloud Services Benefits

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

After completing this course, students will be able to:

  • 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.
Need more info ? Email info@koenig-solutions.com  or   Enquire now!
Delhi
Bangalore (Bengaluru)
Goa
Shimla (Himalayas)
Chennai
Dehradun
Dubai
USA Singapore
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