20776: Performing Big Data Engineering on Microsoft Cloud Services

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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.

Objectives:

  • 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.

Destinatários

  • 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.

Pré-Requisitos

  • 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.

Programa

  • Architectures for Big Data Engineering with Azure
  • Processing Event Streams using Azure Stream Analytics
  • Performing custom processing in Azure Stream Analytics
  • Managing Big Data in Azure Data Lake Store
  • Processing Big Data using Azure Data Lake Analytics
  • Implementing custom operations and monitoring performance in Azure Data Lake Analytics
  • Implementing Azure SQL Data Warehouse
  • Performing Analytics with Azure SQL Data Warehouse
  • Automating the Data Flow with Azure Data Factory

Architectures for Big Data Engineering with Azure

  • Understanding Big Data
  • Architectures for Processing Big Data
  • Considerations for designing Big Data solutions

Processing Event Streams using Azure Stream Analytics

  • Introduction to Azure Stream Analytics
  • Configuring Azure Stream Analytics jobs

Performing custom processing in Azure Stream Analytics

  • Implementing Custom Functions
  • Incorporating Machine Learning into an Azure Stream Analytics Job

Managing Big Data in Azure Data Lake Store

  • Using Azure Data Lake Store
  • Monitoring and protecting data in Azure Data Lake Store

Processing Big Data using Azure Data Lake Analytics

  • Introduction to Azure Data Lake Analytics
  • Analyzing Data with U-SQL
  • Sorting, grouping, and joining data

Implementing custom operations and monitoring performance in Azure Data Lake Analytics

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

Implementing Azure SQL Data Warehouse

  • Introduction to Azure SQL Data Warehouse
  • Designing tables for efficient queries
  • Importing Data into Azure SQL Data Warehouse

Performing Analytics with Azure SQL Data Warehouse

  • Querying Data in Azure SQL Data Warehouse
  • Maintaining Performance
  • Protecting Data in Azure SQL Data Warehouse

Automating the Data Flow with Azure Data Factory

  • Introduction to Azure Data Factory
  • Transferring Data
  • Transforming Data
  • Monitoring Performance and Protecting Data
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20776: Performing Big Data Engineering on Microsoft Cloud Services

Base de Dados | 35h - Laboral: 09h30 - 17h30


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