Engineering Data with Microsoft Cloud Services

Learn via: Classroom / Attend from Anywhere

Price: £2442

Dates and Locations

About this Course

Special Notices

Please note: for Attend from Anywhere customers an additional screen is required. The additional screen must have a minimum screen size of 19 inch and minimum resolution of 1280x1024, with the vertical resolution (1024) being the most critical.

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.

Target Audience

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.

What's Included

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Detailed Information

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

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.

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.

Please note: for Attend from Anywhere customers an additional screen is required. The additional screen must have a minimum screen size of 19 inch and minimum resolution of 1280x1024, with the vertical resolution (1024) being the most critical.

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