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Implementing an Azure Data Solution (DP-200)

In this 3- days course, the students will be able to implement various data platform technologies into solutions that are in line with business and technical needs together with on-premises, cloud, and hybrid data scenarios incorporating both relational and No-SQL data. Students will also get trained on how to process data using a range of technologies and languages for each streaming and batch data.

  • Course Objective
  • Who Can Benefit
  • Prerequisites
  • Syllabus
  • Implement data storage solutions
  • Manage and develop data processing
  • Monitor data solutions
  • Manage data security
  • Manage and troubleshoot Azure data solutions
Primary audience 

  • Data Professionals
  • Data Architects
  • Business Intelligence


    Secondary audience
  • Individuals who develop applications that deliver content from the data platform technologies that exist on Microsoft Azure.

  • Technical knowledge equivalent to Azure fundamentals
Module 1: Azure for the Data Engineer

This module explores how the world of data has evolved and how cloud data 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

After completing this module, students will be able to:

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

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

After completing this module, students will be able to:

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

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

After completing this module, students will be able to:

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

After completing this module, students will be able to:

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 Azure Cosmos DB in Visual Studio Code
Distribute your 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

After completing this module, students will be able to:

Use Azure SQL Database
Describe Azure Data Warehouse
Creating and Querying an Azure SQL Data Warehouse
Using 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

After completing this module, students will be able to:

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

After completing this module, students will be able to:

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

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

After completing this module, students will be able to:

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

After completing this module, students will be able to:

Explain the monitoring capabilities that are available
Troubleshoot common data storage issues
Troubleshoot common data processing issues
Manage disaster recovery
 Microsoft
  • Course Code: DP-200
  • Duration : 3 Days
  • Certified by: Microsoft

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