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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About Wrexham

Wrexham                                   

Wrexham is the largest town located in the north of Wales and also an educational, commercial, administrative, commercial and retail centre. Wrexham is located between lower Dee valley alongside England border and Weish Mountains. Historically it is the part of Denbighshire, the town became part of Clwyd in 1974, and since 1996 it has been the centre of Wrexham County Borough. According to 2011 census, Wrexham had a population of 61,603 which made it a fourth largest urban area in Wales.

History

Council purchased Parciau in the year 1907, and it later turned into a Public Park. In 1910 first cinema in Wrexham was opened. The population of Wrexham continued to grow drastically. In 1901 population was 14,966 and by 1931 it reached 18,567. The population of Wrexham crossed 40,000 for the first time in the year 1981. First-time electricity was generated in the year 1900 in Wrexham. In 1907 electric trams replaced horse-drawn trams and in 1927 they were replaced by buses.

In 1913-1917 Garden Village was built in Wrexham. In the 1920s and 1930s Wrexham council started working for slum clearance. At that time new council house estate has been constructed at Action Park. Other council estates were built at Maes Y Dre and Spring Lodge in 1930s. In 1965 boundaries of Wrexham was extended. In the 1930s at Queens Park, council estate was built. Another was established at Bryn Offa. Action Park estate was extended in the 1960s.

In 1911 Gresford Collery was opened. An explosion and fire accident at Gresford Collery in 1934 killed 261 miners, and three rescuers also died. In late 20th century, traditional industries declined in Wrexham. Coal mining almost ended. Gresford Collary closed in 1973. In 1986 Bersham Collery was closed. New industries came into existence in Wrexham including Pharmaceuticals, engineering, chemicals, electronics and food processing. During Second World War, a big ordnance factory was built at Wrexham, and it was converted into industrial estate after 1945. In 1983 Bersham Heritage Centre was opened. In 1985 Maelor Hospital was opened. The swimming pool was constructed in 1970. In 1998 it was refurbished and renamed as Waterworld Leisure Complex. In 1999 two new shopping centres were opened in Wrexham named Henblas Square and Island Green. First Wrexham Science Festival was held in 1998. In the 21st century, Wrexham is still a developing city. In 2002 Border Retail Park was opened. In 2008 Meadow Shopping Centre was opened. Now Wrexham has a population of 43000.

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