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Perform Big Data Engineering On Microsoft Cloud Services (M20776)

Key points about this course


Duration: 5 Days*

Exam: Perform Big Data Engineering on Microsoft Cloud Services

Accredited: Yes

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  • Microsoft accredited training provided by the largest training company globally
  • Receive experienced tuition from our expert Microsoft training instructors
  • Our Microsoft training courses are fully accredited by Microsoft

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Course Information

Perform Big Data Engineering on Microsoft Cloud Services Course Overview | Azure Training | M20776

This 5-day course is intended to teach delegates 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 Performing Big Data Engineering on Microsoft Cloud Services course is fully accredited by Microsoft through the Microsoft Silver Partnership held by The Knowledge Academy.

After completing this course, delegates 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 optimise 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

Perform Big Data Engineering on Microsoft Cloud Services Course Outline | Azure Training | M20776

This course includes the following modules:

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
  • Analysing 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 optimise jobs.

Lessons:

  • Incorporating custom functionality into Analytics jobs
  • Managing and Optimising jobs

Lab: Implementing custom operations and monitoring performance in Azure Data Lake Analytics

  • Custom extractor
  • Custom processor
  • Integration with R/Python
  • Monitor and optimise 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

Who Should Attend this Course?

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.

Prerequisites

Before attending this course, delegates should possess or be able to demonstrate:

  • 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

 

 

Please arrive at the venue at 8:45am.
Before attending this course, delegates should possess or be able to demonstrate: 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.
We are able to provide support via phone & email prior to attending, during and after the course.
Delegate pack consisting of course notes and exercises, Manual, Experienced Instructor, and Refreshments
This course is 5 days
Once your booking has been placed and confirmed, you will receive an email which contains your course location, course overview, pre-course reading material (if required), course agenda and payment receipts

The Knowledge Academy does not provide an examination for this course. Delegates will be given access to:

  • Tuition from one of our expert trainers
  • Certificate of completion
  • Refreshments

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Key points about this course


Duration: 5 Days*

Exam: Perform Big Data Engineering on Microsoft Cloud Services

Accredited: Yes


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