MOC On-Demand: 20776-Engineering Data with Microsoft Cloud Services Course Outline
Please note: This course aligns to Microsoft exam 70-776. Exam 70-776 retired 06-30-2019.
*** Note: This is an On-Demand Self Study Class, 5-days of content, 90-days unlimited access, $995 ***
You can take this class at any time; there are no set dates. It covers the same content as the 5-day instructor-led class of the same name. The cost for this MOC On-Demand class is $995. (Applicable State and Local taxes may be added for On-Demand purchases, depending on your location.) Microsoft Enterprise customers paying with Software Assurance Vouchers, see SATV Payment note below.
MOC On-Demand Learner Profiles
MOC On-Demand is a self-study training solution that was designed for two types of learners. First, MOC On-Demand is a great fit for experienced IT professionals who don't need a traditional 5-day class to upgrade their existing skills. They can pick and choose topics to make the most effective use of their time. Second, MOC On-Demand is perfect for highly-motivated individuals who are new to a technology and need to space their learning over a period of weeks or months. These learners can take their time and repeat sections as needed until they master the new concepts.
About MOC On-Demand
Our MOC On-Demand classes are self-study courses with 30 to 40 hours of content. They include hours of videos, hands-on labs using the actual software, and knowledge checks and were created by Microsoft to mirror the content found in the traditional live instructor-led version of this course. Those features are all part of the standard MOC On-Demand training. But don't settle for the standard MOC On-Demand class! Check out the "ONLC Extras" that you get when purchasing this course from us.
ONLC Training Centers bundles in valuable extras with our MOC On-Demand Courses. These items are not available from other training companies.
Courseware After the Course.
Get the digital courseware that is used in the live, instructor-led version of this class. While the MOC On-Demand access goes away after 90 days, you will have access to the "extra" digital courseware for an unlimited period of time.
24/7 Online Support.
You will be able to chat online with a content matter expert while you are taking your MOC On-Demand class. And, with your permission, the expert can even take over your computer to provide with assistance with your labs.
These add-ons are available exclusively by ONLC Training Centers and are offered to you at an additional cost.
Upgrade to 180-Day Access, $250.
Need more time? Extend the base 90-day access to 180 days on your MOC On-Demand class, complete with labs, videos and knowledge checks. Extension must be ordered at time of original purchase and is non-refundable.
Certification Pak, $150.
Interested in obtaining certification? Get a Transcender practice exam and a Microsoft exam voucher at this reduced price.
ILT Listener, $250.
Want to listen in and follow along with a live Instructor-Led Training (ILT) class? We offer this option for individuals on a limited budget who have time during the day to hear a live class in progress. ILT Listeners have access to their online support chat expert during the class but they do not have direct access to the live instructor.
ILT Participant, $ Varies.
You've purchased MOC On-Demand, have gone through the training and decided that you still want a live class. Just pay difference between MOC On-Demand course and and the Instructor-Led Training (ILT) class and you can have a seat in our live class. Get both self-study and live, instructor-led training for the retail price of the instructor-led class alone!
Paying with Software Assurance Training Vouchers (SATV)
For Microsoft Enterprise customers paying with Software Assurance Vouchers, the cost of this class is 5 vouchers--this includes access to the self-study materials, the student workbook, 24/7 access to an online expert, and a corresponding exam voucher, if applicable, upon request.
Do You Still Prefer a Live, Instructor-led Class?
Already know MOC On-Demand is not right for you? We also offer this same course content in a live, instructor-led format. For more details, click on the link below:
This five-day instructor-led 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.
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.
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.
Special Note to New Hampshire Residents
This course has not yet been approved by the State's Department of Education. Please contact us to get an update as to when the class should be available in New Hampshire.
At course completion
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.
Module 1: Architectures for Big Data Engineering with Azure
This module describes common architectures for processing big data using Azure tools and services.
Understanding Big Data
Architectures for Processing Big Data
Considerations for designing Big Data solutions
Lab : Designing a Big Data Architecture
Design a big data architecture
Module 2: Processing Event Streams using Azure Stream Analytics
This module describes how to use Azure Stream Analytics to design and implement stream processing over large-scale data.
Introduction to Azure Stream Analytics
Configuring Azure Stream Analytics jobs
Lab : Processing Event Streams with Azure Stream Analytics
Create an Azure Stream Analytics job
Create another Azure Stream job
Add an Input
Edit the ASA job
Determine the nearest Patrol Car
Module 3: Performing custom processing in Azure Stream Analytics
This module describes how to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job.
Implementing Custom Functions
Incorporating Machine Learning into an Azure Stream Analytics Job
Lab : Performing Custom Processing with Azure Stream Analytics
Add logic to the analytics
Detect consistent anomalies
Determine consistencies using machine learning and ASA
Module 4: Managing Big Data in Azure Data Lake Store
This module describes how to use Azure Data Lake Store as a large-scale repository of data files.
Using Azure Data Lake Store
Monitoring and protecting data in Azure Data Lake Store
Lab : Managing Big Data in Azure Data Lake Store
Update the ASA Job
Upload details to ADLS
Module 5: Processing Big Data using Azure Data Lake Analytics
This module describes how to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store.
Introduction to Azure Data Lake Analytics
Analyzing Data with U-SQL
Sorting, grouping, and joining data
Lab : Processing Big Data using Azure Data Lake Analytics
Query against Database
Calculate average speed
Module 6: Implementing custom operations and monitoring performance in Azure Data Lake Analytics
This module describes how to create and deploy custom functions and operations, integrate with Python and R, and protect and optimize jobs.
Incorporating custom functionality into Analytics jobs
Managing and Optimizing jobs
Lab : Implementing custom operations and monitoring performance in Azure Data Lake Analytics
Integration with R/Python
Monitor and optimize a job
Module 7: Implementing Azure SQL Data Warehouse
This module describes how to use Azure SQL Data Warehouse to create a repository that can support large-scale analytical processing over data at rest.
Introduction to Azure SQL Data Warehouse
Designing tables for efficient queries
Importing Data into Azure SQL Data Warehouse
Lab : Implementing Azure SQL Data Warehouse
Create a new data warehouse
Design and create tables and indexes
Import data into the warehouse.
Module 8: Performing Analytics with Azure SQL Data Warehouse
This module describes how to import data in Azure SQL Data Warehouse, and how to protect this data.
Querying Data in Azure SQL Data Warehouse
Protecting Data in Azure SQL Data Warehouse
Lab : Performing Analytics with Azure SQL Data Warehouse
Performing queries and tuning performance
Integrating with Power BI and Azure Machine Learning
Configuring security and analysing threats
Module 9: Automating the Data Flow with Azure Data Factory
This module describes how to use Azure Data Factory to import, transform, and transfer data between repositories and services.
Introduction to Azure Data Factory
Monitoring Performance and Protecting Data
Lab : Automating the Data Flow with Azure Data Factory
Automate the Data Flow with Azure Data Factory
After completing this module, students will be able to:
Describe the purpose of Azure Data Factory, and explain how it works.
Describe how to create Azure Data Factory pipelines that can transfer data efficiently.
Describe how to perform transformations using an Azure Data Factory pipeline.
Describe how to monitor Azure Data Factory pipelines, and how to protect the data flowing through these pipelines.
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