Beskrivelse

På dette kursus er arkitektur og sikkerhed for Azure data storage løsninger i fokus. Der tages udgangspunkt i de storage muligeder, som blev præsenteret på DP-200. Kursisten indføres i at designe procesarkitekturer ved hjælp af en række teknologier til både streaming- og batchdata. Man arbejder med flere typer data, herunder relational-, NoSQL- eller Data Warehouse data i on-premises, cloud- eller hybridløsninger. Yderligere lærer kursisten at skabe rammerne for datasikkerhed i ens dataløsning; her skal man bl.a. forstå data access og data policies. Ved processering af data kan der være et ønske om, at løsningen er skalerbar eller kører i realtid. Det er også vigtigt at indtænke disaster-recovery i arkitekturen. Endelig er det også centralt at overvåge omkostninger i Azure, så man får fuldt udbytte af de ressourcer, man betaler for. Kursisten får best practices til at kunne designe skalerbare Azure Data løsninger, som muliggøre optimization, availability og disaster recovery of big data, batch processing og streaming data solutions.

Indhold

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
• 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 : 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 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
• 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 Module 4: Data Platform Security Design Considerations
• In this module, the student will learn how to incorporate security into an architecture design and learn the key decision points in Azure provides to help you create a secure environment through all the layers of your architecture.
• Lessons
• 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 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
• Adjust Workload Capacity by Scaling
• Optimize Network Performance
• Design for Optimized Storage and Database Performance
• Identifying 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 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