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Microsoft MCA: Azure Data Engineer Associate (DP-200/DP-201)

Course Length: 5 days
Certifications: MCA: Azure Data Engineer Associate
Number of Exams: 2

Class Schedule
04/27/20 - 05/01/20

Info/Enroll

07/20/20 - 07/24/20

Info/Enroll

10/12/20 - 10/16/20

Info/Enroll

Exam DP-200 & DP-201: Azure Data Engineers design and implement the management, monitoring, security, and privacy of data using the full stack of Azure data services to satisfy business needs.


Course DP-200T01: Implementing an Azure Data Solution

In this course, the students will implement various data platform technologies into solutions that are in-line with business and technical requirements, including on-premises, cloud, and hybrid data scenarios incorporating both relational and NoSQL data. They will also learn how to process data using a range of technologies and languages for both streaming and batch data.

The students will also explore how to implement data security, including authentication, authorization, data policies, and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing, and streaming data solutions.

Audience profile

The primary audience for this course is Data Professionals, Data Architects, and Business Intelligence Professionals who want to learn about the data platform technologies that exist on Microsoft Azure.The secondary audience for this course is individuals who develop applications that deliver content from the data platform technologies that exist on Microsoft Azure.

Prerequisites

In addition to their professional experience, students who take this training should have technical knowledge equivalent to the following courses:

  • Azure Fundamentals

Course outline

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 businesses to explore their data in different ways. The students will gain an overview of the various data platform technologies that are available and how a Data Engineer's role and responsibilities has evolved to work in this new world to an organization's benefit.

  • 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 students 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 want to be stored 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.

  • 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 how to perform data preparation task that can contribute to the data science project.

  • 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, 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.

  • 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 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 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 students will be able explain why they would choose one service over another, and how to provision, connect, and manage each of the services.

  • 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
  • Create and Query 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, they will learn how to manage and monitor running jobs.

  • 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
  • Understand Data Ingestion with Event Hubs
  • Understand 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.

  • 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:

  • Understand Azure Data Factory and Databricks
  • Understand Azure Data Factory Components
  • Be able to 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 data. The students will explore how security can range from setting up secure networks and access keys, to defining permission, to monitoring across a range of data stores.

  • 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:

  • Have an introduction to security
  • Understand key security components
  • Understand securing Storage Accounts and Data Lake Storage
  • Understand securing Data Stores
  • Understand securing Streaming Data
Module 9: Monitoring and Troubleshooting Data Storage and Processing

In this module, the students 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.

  • 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

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Course DP-201T01: Designing an Azure Data Solution

In this course, the students will design various data platform technologies into solutions that are in line with business and technical requirements. This can include on-premises, cloud, and hybrid data scenarios which incorporate relational, NoSQL, or Data Warehouse data. They will also learn how to design process architectures using a range of technologies for both streaming and batch data. The students will also explore how to design data security, including data access, data policies, and standards. They will also design Azure data solutions, which includes the optimization, availability, and disaster recovery of big data, batch processing, and streaming data solutions.

Audience profile

The audience for this course is Data Professionals, Data Architects, and Business Intelligence Professionals who want to learn about the data platform technologies that exist on Microsoft Azure. The secondary audience for this course is individuals who develop applications that deliver content from the data platform technologies that exist on Microsoft Azure.

Prerequisites

In addition to their professional experience, students who take this training should have technical knowledge equivalent to the following courses:

  • Azure Fundamentals
  • DP-200: Implementing an Azure Data Solution

Course outline

Module 1: Data Platform Architecture Considerations

In this module, the students will learn how to design and build secure, scalable, and performant solutions in Azure by examining the core principles found in every good architecture. They will learn how using key principles throughout architecture, regardless of technology choice, can help you design, build, and continuously improve the architecture for an organization's benefit.

  • Core Principles of Creating Architectures
  • Design with Security in Mind
  • Performance and Scalability
  • Design for availability and recoverability
  • Design for efficiency and operations
  • Case Study

Lab : Case Study

  • Design with security in mind
  • Consider performance and scalability
  • Design for availability and recoverability
  • Design for efficiency and operations

After completing this module, students will be able to:

  • Design with security in mind
  • Consider performance and scalability
  • Design for availability and recoverability
  • Design for efficiency and operations
Module 2: Azure Batch Processing Reference Architectures

In this module, students will learn the reference design and architecture patterns for dealing with the batch processing of data. The student will be exposed to dealing with the movement of data from on-premises systems into a cloud data warehouse and how it can be automated. The students will also be exposed to an AI architecture and how the data platform can integrate with an AI solution.

  • Lambda architectures from a Batch Mode Perspective
  • Design an Enterprise BI solution in Azure
  • Automate enterprise BI solutions in Azure
  • Architect an Enterprise-grade Conversational Bot in Azure

Lab : Architect an Enterprise-grade Conversational Bot in Azure

  • Designing an Enterprise BI solution in Azure
  • Automate an Enterprise BI solution in Azure
  • Automate an Enterprise BI solution in Azure

After completing this module, students will be able to:

  • Understand the core principles of creating architectures
  • Describe Lambda architectures from a Batch Mode Perspective
  • Design an Enterprise BI solution in Azure
  • Automate enterprise BI solutions in Azure
  • Architect an Enterprise-grade conversational bot in Azure
Module 3: Azure Real-Time Reference Architectures

In this module, the students will learn the reference design and architecture patterns for dealing with streaming data. They will learn how streaming data can be ingested by Event Hubs and Stream Analytics to deliver real-time analysis of data. They will also explore a data science architecture that streams data into Azure Databricks to perform trend analysis. They will finally learn how an Internet of Things (IoT) architecture will require data platform technologies to store data.

  • Describe Lambda architectures for a Real-Time Perspective
  • Architect a stream processing pipeline with Azure Stream Analytics
  • Design a stream processing pipeline with Azure Databricks
  • Create an Azure IoT reference architecture

Lab : Azure Real-Time Reference Architectures

  • Architect a stream processing pipeline with Azure Stream Analytics
  • Design a stream processing pipeline with Azure Databricks
  • Create an Azure IoT reference architecture

After completing this module, students will be able to:

  • Describe Lambda architectures for a Real-Time Mode Perspective
  • Architect a stream processing pipeline with Azure Stream Analytics
  • Design a stream processing pipeline with Azure Databricks
  • Create an Azure IoT reference architecture
Module 4: Data Platform Security Design Considerations

In this module, the students will learn how to incorporate security into an architecture design and learn the key decision points in Azure provided to help create a secure environment through all the layers of architecture.

  • Defense in Depth Security Approach
  • Identity Management
  • Infrastructure Protection
  • Encryption Usage
  • Network Level Protection
  • Application Security

Lab : Data Platform Security Design Considerations

  • Defense in Depth Security Approach
  • Identity Protection

After completing this module, students will understand the following:

  • Defense in Depth Security Approach
  • Identity Management
  • Infrastructure Protection
  • Encryption Usage
  • Network Level Protection
  • Application Security
Module 5: Designing for Resiliency and Scale

In this module, students will learn scaling services to handle load. They will learn how identifying network bottlenecks and optimizing storage performance are important to ensure users have the best experience. They will also learn how to handle infrastructure and service failure, recover from the loss of data, and recover from a disaster by designing availability and recoverability into the architecture.

  • Adjust Workload Capacity by Scaling
  • Optimize Network Performance
  • Design for Optimized Storage and Database Performance
  • Identify Performance Bottlenecks
  • Design a Highly Available Solution
  • Incorporate Disaster Recovery into Architectures
  • Design Backup and Restore strategies

Lab : Designing for Resiliency and Scale

  • Adjust Workload Capacity by Scaling
  • Design for Optimized Storage and Database Performance
  • Design a Highly Available Solution
  • Incorporate Disaster Recovery into Architectures

After completing this module, students will be able to:

  • Adjust Workload Capacity by Scaling
  • Optimize Network Performance
  • Design for Optimized Storage and Database Performance
  • Identify Performance Bottlenecks
  • Design a Highly Available Solution
  • Incorporate Disaster Recovery into Architectures
  • Design Backup and Restore strategies
Module 6: Design for Efficiency and Operations

In this module, students will learn how to design an Azure architecture that is operationally-efficient and minimizes costs by reducing spend and they will understand how to design architectures that eliminate waste and gives them full visibility into what is being utilized in the organization's Azure environment.

  • Maximizing the Efficiency of your Cloud Environment
  • Use Monitoring and Analytics to Gain Operational Insights
  • Use Automation to Reduce Effort and Error

Lab : Design for Efficiency and Operations

  • Maximize the Efficiency of your Cloud Environment
  • Use Monitoring and Analytics to Gain Operational Insights
  • Use Automation to Reduce Effort and Error

After completing this module, students will be able to:

  • Maximize the Efficiency of your Cloud Environment
  • Use Monitoring and Analytics to Gain Operational Insights
  • Use Automation to Reduce Effort and Error

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