Description

PART 1

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 and Machine Learning Platforms
• Describe the Team Data Science Process
• Provision Azure Databricks and workspaces
• Perform data preparation tasks

Lab : Enabling Team Based Data Science with Azure Databricks

• Explain Azure Databricks and Machine Learning Platforms
• Describe the Team Data Science Process
• Provision Azure Databricks and Workspaces
• Perform Data Preparation Tasks

After completing this module, students will be able to:

• Explain Azure Databricks
• Describe the Team Data Science Process
• Provision Azure Databricks and workspaces
• Perform data preparation tasks

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
• Provision 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

• SQL Database and SQL Data Warehouse
• Provision an Azure SQL database to store data
• Provision and load data into Azure SQL Data Warehouse

Lab : Working with Relational Data Stores in the Cloud

• Explain SQL Database and SQL Data Warehouse
• Create an Azure SQL Database to store data
• Provision and load data into Azure SQL Data Warehouse

After completing this module, students will be able to:

• Explain SQL Database and SQL Data Warehouse
• Provision an Azure SQL database to store application data
• Provision and load data in Azure SQL Data Warehouse
• Import data into Azure SQL Data Warehouse using PolyBase

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
• Querying streaming data using Stream Analytics
• How to process data with Azure Blob and Stream Analytics
• How to process data with Event Hubs and Stream Analytics

Lab : Performing Real-Time Analytics with Stream Analytics

• Explain data streams and event processing
• Querying streaming data using Stream Analytics
• Process data with Azure Blob and Stream Analytics
• Process data with Event Hubs and Stream Analytics

After completing this module, students will be able to:

• Explain data streams and event processing
• Querying streaming data using Stream Analytics
• How to process data with Event Hubs and Stream Analytics
• How to process data with Azure Blob and Stream Analytics

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
• Create Linked Services and datasets
• Create pipelines and activities
• Azure Data Factory pipeline execution and triggers

Lab : Orchestrating Data Movement with Azure Data Factory

• Explain how Data Factory Works
• Create Linked Services and Datasets
• Create Pipelines and Activities
• Azure Data Factory Pipeline Execution and Triggers

After completing this module, students will be able to:

• Explain how Azure Data Factory works
• Create Linked Services and Datasets
• Create Pipelines and Activities
• Azure Data Factory pipeline execution and triggers

Module 8: Securing Azure Data Platforms

In this module, students will learn how Azure Storage 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 with Advanced Threat Detection.

Lessons

• Configuring Network Security
• Configuring Authentication
• Configuring Authorization
• Auditing Security

Lab : Securing Azure Data Platforms

• Configure network security
• Configure Authentication
• Configure Authorization
• Explore SQL Server Books Online

After completing this module, students will be able to:

• Configure Authentication
• Use storage account keys
• Use shared access signatures
• Configure Authorization
• Control network access
• Understand transport-level encryption with HTTPS
• Understand Advanced Threat Detection

Module 9: Monitoring and Troubleshooting Data Storage and Processing

In this module, the student will look at the wide range of monitoring capabilities that are available to provide operational support should there be issue with a data platform architecture. They will explore the data engineering troubleshooting approach and be able to apply this to common data storage and data processing issues.

Lessons

• Data Engineering troubleshooting approach
• Azure Monitoring Capabilities
• Troubleshoot common data issues
• Troubleshoot common data processing issues

Lab : Monitoring and Troubleshooting Data Storage and Processing

• Explain the Data Engineering troubleshooting approach
• Explain the monitoring capabilities that are available
• Troubleshoot common data storage issues
• Troubleshoot common data processing issues

After completing this module, students will be able to:

• Explain the monitoring capabilities that are available
• Explain the Data Engineering troubleshooting approach
• Troubleshoot common data storage issues
• Troubleshoot common data processing issues

Module 10: Integrating and Optimizing Data Platforms

In this module, the student will explore the various ways in which data platforms can be integrated based upon different business requirements. They will also explore the various ways in which data platforms can be optimized from a storage and data processing perspective to improve data loads. Finally, disaster recovery options are revealed to ensure business continuity.

Lessons

• Integrating data platforms
• Optimizing data stores
• Optimize streaming data
• Manage disaster recovery

Lab : Integrating and Optimizing Data Platforms

• Integrate Data Platforms
• Optimize Data Stores
• Optimize Streaming Data
• Manage Disaster recovery

After completing this module, students will be able to:

• Integrate data platforms
• Optimize relational data stores
• Optimize NoSQL data stores
• Optimize Streaming data stores
• Manage disaster recovery

PART 2

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 your architecture regardless of technology choice, can help you design, build, and continuously improve your architecture for an organizations benefit.

Lessons

• 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

• Core principles for creating architectures
• 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:

• Describe the core principles for creating architectures
• Design with Security in mind
• Consider performance and scalability
• Design for availability and recoverability
• Design for efficiency and operations
• Understand the course Case Study

Module 2: Azure Batch Processing Reference Architectures

In this module, the student 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 student will also be exposed to an AI architecture and how the data platform can integrate with an AI solution.

Lessons

• 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

• Lambda architectures from a Batch Mode Perspective
• 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:

• 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 student 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 the 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.

Lessons

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

Lab : Azure Real-Time Reference Architectures

• 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

After completing this module, students will be able to:

• 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 student will learn how to incorporate security into your architecture design and discover the tools that Azure provides to help you create a secure environment through all the layers of your architecture.

Lessons

• Defense in Depth Security Approach
• Network Level Protection
• Identity Protection
• Encryption Usage
• Advanced Threat Protection

Lab : Data Platform Security Design Considerations

• Defense in Depth Security Approach
• Network Level Protection
• Identity Protection
• Encryption Usage
• Advanced Threat Protection

After completing this module, students will be able to:

• Defense in Depth Security Approach
• Network Level Protection
• Identity Protection
• Encryption Usage
• Advanced Threat Protection

Module 5: Designing for Resiliency and Scale

In this module, student will learn scaling services to handle load. They will learn how identifying network bottlenecks and optimizing your storage performance are important to ensure your 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 your architecture.

Lessons

• Design Backup and Restore strategies
• Optimize Network Performance
• Design for Optimized Storage and Database Performance
• Design for Optimized Storage and Database Performance
• Incorporate Disaster Recovery into Architectures
• Design Backup and Restore strategies

Lab : Designing for Resiliency and Scale

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

After completing this module, students will be able to:

• Adjust Workload Capacity by Scaling
• Optimize Network Performance
• Design for Optimized Storage and Database Performance
• 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, they will understand how to design architectures that eliminates waste and gives them full visibility into what is being utilized in your organizations Azure environment.

Lessons

• 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