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Engineering Data with Microsoft Cloud Services - 20776 Course Outline

 (5 days)

Overview
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.

*** NOTE: This class also available in On-Demand, eLearning Format ***
Too busy at work to miss 5 days out of the office to take this class? Consider the MOC On-Demand version of this course. Watch videos developed by Microsoft, take the same hands-on labs, access an online expert 24/7, and study at on your own time, at your own pace. For details on this alternative format, go to
MOC On-Demand: 20776-Engineering Data with Microsoft Cloud Services

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.

Audience profile
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.

Course Outline

Module 1: Architectures for Big Data Engineering with Azure
This module describes common architectures for processing big data using Azure tools and services.
Lessons
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.
Lessons
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.
Lessons
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.
Lessons
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.
Lessons
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
Add functionality
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.
Lessons
Incorporating custom functionality into Analytics jobs
Managing and Optimizing jobs
Lab : Implementing custom operations and monitoring performance in Azure Data Lake Analytics
Custom extractor
Custom processor
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.
Lessons
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.
Lessons
Querying Data in Azure SQL Data Warehouse
Maintaining Performance
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.
Lessons
Introduction to Azure Data Factory
Transferring Data
Transforming Data
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.
View outline in Word

A20776

Attend hands-on, instructor-led Engineering Data with Microsoft Cloud Services - 20776 training classes at ONLC's more than 300 locations. Not near one of our locations? Attend these same live classes from your home/office PC via our Remote Classroom Instruction (RCI) technology.

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Fee:  $2795

Savings options:

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