Designing an Azure Data Solution

Designing an Azure Data Solution Course Overview

• 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, No-SQL or Data Warehouse data. They will also learn how to design process architectures using a range of technologies for both streaming and batch data.

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• 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.

Skills Gained

• After completing this course, students will be able to:

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• 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

• 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

• 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

• Defense in Depth Security Approach

• Network Level Protection

• Identity Protection

• Encryption Usage

• Advanced Threat Protection

• 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

• Maximize the Efficiency of your Cloud Environment

• Use Monitoring and Analytics to Gain Operational Insights

• Use Automation to Reduce Effort and Error

Who will the Course Benefit?

• 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.

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• The secondary audience for this course is individuals who develop applications that deliver content from the data platform technologies that exist on Microsoft Azure.

Requirements

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

• M-AZ-900T01: Azure Fundamentals

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

NOTE: Course technical content is subject to change without notice.

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Course Contents

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:

Core principles for creating architectures

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, 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:

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





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:

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 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





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





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