A Quick Glance

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    Learn how to use Azure for data solutions

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    Prepare for the Implementing an Azure Data Solution exam

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    Taught by Microsoft Certified Trainers

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    Includes official Microsoft material

Who should take this course

Data professionals, data architects, and business intelligence experts who want to learn more about Microsoft Azure's data platform technologies and Develop applications that deliver content from Microsoft Azure's data platform technologies.

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Prerequisites

There are no prerequisites.

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  Course Overview

In this course, delegates will learn how to implement various data platform technologies in solutions that meet business and technical needs, including on-premise, cloud, and hybrid data scenarios, which include both relational and no-SQL data. Delegates will also learn how to process data using a range of technologies and languages ​​for both streaming and batch data.

This course explores how to implement data security, including authentication, authorisation, data policies and standards,  and implement data solution monitoring for both data storage and data processing. Eventually, they will manage and troubleshoot Azure data solutions, including optimising and restoring large volumes of data, batch processing, and streaming data solutions.

This role-based course can be used to prepare for certification as a Microsoft Azure Data Engineer.

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  Course Content

Module 1: Azure for the Data Engineer

This module explores how the world of data has evolved and how cloud datAudiencea platform technologies are providing new opportunities for business to explore their data in different ways. The student will gain an overview of the various data platform technologies that are available, and how a Data Engineers role and responsibilities has evolved to work in this new world to an organization benefit

Lessons

  • Explain the evolving world of data
  • Survey the services in the Azure Data Platform
  • Identify the tasks that are performed by a Data Engineer
  • Describe the use cases for the cloud in a Case Study

Lab: Azure for the Data Engineer

  • Identify the evolving world of data
  • Determine the Azure Data Platform Services
  • Identify tasks to be performed by a Data Engineer
  • Finalize the data engineering deliverables

Module 2: Working with Data Storage

 This module teaches the variety of ways to store data in Azure. The Student will learn the basics of storage management in Azure, how to create a Storage Account, and how to choose the right model for the data you want to store in the cloud. They will also understand how data lake storage can be created to support a wide variety of big data analytics solutions with minimal effort.

Lessons

  • Choose a data storage approach in Azure
  • Create an Azure Storage Account
  • Explain Azure Data Lake storage
  • Upload data into Azure Data Lake

Lab: Working with Data Storage

  • Choose a data storage approach in Azure
  • Create a Storage Account
  • Explain Data Lake Storage
  • Upload data into Data Lake Store

 

Module 3: Enabling Team Based Data Science with Azure Databricks

This module introduces students to Azure Databricks and how a Data Engineer works with it to enable an organization to perform Team Data Science projects. They will learn the fundamentals of Azure Databricks and Apache Spark notebooks; how to provision the service and workspaces and learn how to perform data preparation task that can contribute to the data science project.

Lessons

  • Explain Azure Databricks
  • Work with Azure Databricks
  • Read data with Azure Databricks
  • Perform transformations with Azure Databricks

Lab: Enabling Team Based Data Science with Azure Databricks

  • Explain Azure Databricks
  • Work with Azure Databricks
  • Read data with Azure Databricks
  • Perform transformations with Azure Databricks

 

Module 4: Building Globally Distributed Databases with Cosmos DB

In this module, students will learn how to work with NoSQL data using Azure Cosmos DB. They will learn how to provision the service, and how they can load and interrogate data in the service using Visual Studio Code extensions, and the Azure Cosmos DB .NET Core SDK. They will also learn how to configure the availability options so that users are able to access the data from anywhere in the world.

Lessons

  • Create an Azure Cosmos DB database built to scale
  • Insert and query data in your Azure Cosmos DB database
  • Build a .NET Core app for Cosmos DB in Visual Studio Code
  • Distribute your data globally with Azure Cosmos DB

Lab: Building Globally Distributed Databases with Cosmos DB

  • Create an Azure Cosmos DB
  • Insert and query data in Azure Cosmos DB
  • Build a .Net Core App for Azure Cosmos DB using VS Code
  • Distribute data globally with Azure Cosmos DB

 

Module 5: Working with Relational Data Stores in the Cloud

In this module, students will explore the Azure relational data platform options including SQL Database and SQL Data Warehouse. The student will be able explain why they would choose one service over another, and how to provision, connect and manage each of the services.

Lessons

  • Use Azure SQL Database
  • Describe Azure SQL Data Warehouse
  • Creating and Querying an Azure SQL Data Warehouse
  • Use PolyBase to Load Data into Azure SQL Data Warehouse

Lab: Working with Relational Data Stores in the Cloud

  • Use Azure SQL Database
  • Describe Azure SQL Data Warehouse
  • Creating and Querying an Azure SQL Data Warehouse
  • Use PolyBase to Load Data into Azure SQL Data Warehouse

 

Module 6: Performing Real-Time Analytics with Stream Analytics

In this module, students will learn the concepts of event processing and streaming data and how this applies to Events Hubs and Azure Stream Analytics. The students will then set up a stream analytics job to stream data and learn how to query the incoming data to perform analysis of the data. Finally, you will learn how to manage and monitor running jobs.

Lessons

  • Explain data streams and event processing
  • Data Ingestion with Event Hubs
  • Processing Data with Stream Analytics Jobs

Lab: Performing Real-Time Analytics with Stream Analytics

  • Explain data streams and event processing
  • Data Ingestion with Event Hubs
  • Processing Data with Stream Analytics Jobs

 

Module 7: Orchestrating Data Movement with Azure Data Factory

In this module, students will learn how Azure Data factory can be used to orchestrate the data movement and transformation from a wide range of data platform technologies. They will be able to explain the capabilities of the technology and be able to set up an end to end data pipeline that ingests and transforms data.

Lessons

  • Explain how Azure Data Factory works
  • Azure Data Factory Components
  • Azure Data Factory and Databricks

Lab: Orchestrating Data Movement with Azure Data Factory

  • Explain how Data Factory Works
  • Azure Data Factory Components
  • Azure Data Factory and Databricks

 

Module 8: Securing Azure Data Platforms

In this module, students will learn how Azure provides a multi-layered security model to protect your data. The students will explore how security can range from setting up secure networks and access keys, to defining permission through to monitoring across a range of data stores.

Lessons

  • An introduction to security
  • Key security components
  • Securing Storage Accounts and Data Lake Storage
  • Securing Data Stores
  • Securing Streaming Data

Lab: Securing Azure Data Platforms

  • An introduction to security
  • Key security components
  • Securing Storage Accounts and Data Lake Storage
  • Securing Data Stores
  • Securing Streaming Data

 

Module 9: Monitoring and Troubleshooting Data Storage and Processing

In this module, the student will get an overview of the range of monitoring capabilities that are available to provide operational support should there be issue with a data platform architecture. They will explore the common data storage and data processing issues. Finally, disaster recovery options are revealed to ensure business continuity.

Lessons

  • Explain the monitoring capabilities that are available
  • Troubleshoot common data storage issues
  • Troubleshoot common data processing issues
  • Manage disaster recovery

Lab: Monitoring and Troubleshooting Data Storage and Processing

  • Explain the monitoring capabilities that are available
  • Troubleshoot common data storage issues
  • Troubleshoot common data processing issues
  • Manage disaster recovery
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In 1973 Monkwearmouth Station Museum was opened. In 1974 North East Aircraft Museum was founded and new General Hospital was opened in Sunderland in 1978. Sunderland suffered in the 1930s when third of the men were unemployed. During 1950s Joblessness lowered and in 1980s unemployment returned. In late 20th century, Sunderland’s coal mining declined rapidly. After 1986 no more coal was exported. New industries replaced the old ones. Sunderland is well known for its car making industry. Other industries in Sunderland include electronic engineering, papermaking, mechanical engineering and textiles. Sunderland was made a city in the year 1992. In 1995 Sunderland Library and Arts Centre was opened. In 1997 Stadium of Light was opened and in 1998 National Glass Centre was opened. In 2002 Tyne and Wear Metro was expanded to Sunderland. In the beginning years of 21st century, Sunniside area was regenerated. Sunderland Aquatic Centre was opened in 2008. Now the population of Sunderland is 275,000.

 

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