Performing Big Data Engineering on Microsoft Cloud Services (20776)

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.

Retail Price: $2,625.00

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Course Days: 5


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About this Course

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.

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.

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.

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.

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

After completing this module, students will be able to:

  • Explain the concept of Big Data.
  • Describe the Lambda and Kappa architectures.
  • Describe design considerations for building Big Data Solutions with Azure.

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

After completing this module, students will be able to:

  • Describe the purpose and structure of Azure Stream Analytics.
  • Configure Azure Stream Analytics jobs for scalability, reliability and security.

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

After completing this module, students will be able to:

  • Describe how to create and use custom functions in Azure Stream Analytics.
  • Describe how to use Azure Machine Learning models in an Azure Stream Analytics job.

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

After completing this module, students will be able to:

  • Describe how to create an Azure Data Lake Store, create folders, and upload data.
  • Explain how to monitor an Azure Data Lake account, and protect the data that it contains.

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

After completing this module, students will be able to:

  • Describe the purpose of Azure Data Lake Analytics, and how to create and run jobs.
  • Describe how to use USQL to process and analyse data.
  • Describe how to use windowing to sort data and perform aggregated operations, and how to join data from multiple sources.

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

After completing this module, students will be able to:

  • Describe how to incorporate custom features and assemblies into USQL.
  • Describe how to implement security to protect jobs, and how to monitor and optimize jobs to ensure efficient operations.

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.

After completing this module, students will be able to:

  • Describe the purpose and structure of Azure SQL Data Warehouse.
  • Describe how to design table to optimize the processing performed by the data warehouse.
  • Describe tools and techniques for importing data into a warehouse at scale.

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

After completing this module, students will be able to:

  • Describe how to perform queries and use the data held in a data warehouse to perform analytics and generate reports.
  • Describe how to configure and monitor a data warehouse to maintain good performance.
  • Describe how to protect data and manage security in a data warehouse.

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.

Now you can build Microsoft technical experience while balancing the demands of your schedule. MOC On-Demand combines high-quality video, reading, live hands-on labs and knowledge checks in a self-paced format to help you build skills on Microsoft technologies as you schedule allows – all at once or five minutes at a time. The modular, self-directed course structure adapts to your learning needs and learning style.

Brought to you by the people who write the software, Microsoft Official Courses On-Demand are only available through certified Microsoft Learning Partners like Tandem Solution.

Our On-Demand Solution Includes all of the following:

  • 90-day access to training videos
  • 90-day access to hands-on labs
  • 90-day access to exercises
  • Knowledge Checks

The benefits of our On-Demand solution are: 

  • MICROSOFT OFFICIAL: The content is based on the same official courseware used in instructor-led training and maps to the MOC flow
  • LIVE HANDS-ON LABS: Unlike other on-demand offerings that offer simulated labs, MOC On-Demand gives you a live, real-time environment for hands-on learning
  • SELF-PACED LEARNING: With MOC On-Demand, you can build deep technical skills on a range of Microsoft technologies when you choose, where you choose
  • HIGH-QUALITY CONTENT: The high-quality videos offer in-depth technical content delivered by engaging instructors
  • IMMEDIATE FFEDBACK: Knowledge checks at the end of each module and each course tells you whether you’ve mastered the content and are ready to move on

On-Demand Add-ons:

  • Lifetime eBook available at an additional cost
  • 180 day access available at an additional cost

*SATV Vouchers are accepted



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