Microsoft DP-201: Designing an Azure Data Solution

În cadrul acestui curs, studenții vor proiecta tehnologii pe platforme de date în soluții care sunt aliniate cu cerințele tehnice și de business. Acest aspect va include scenarii on-premises, cloud și de date hibride care includ date NoSQL sau Data Warehouse.

Cui i se adresează?

Acest curs se adresează Data Professionals, Data Architecs și Business Intelligence Professionals.

Ce veți învăța?

În cadrul acestui curs, studenții vor învăța cum să proiecteze procese de arhitecturi utilizând o gamă de tehnologii pentru streaming și loturi de date.

Cerințe preliminare:

Pentru a participa la acest curs, studenții trebuie să aibă cunoștințe echivalente următoarelor cursuri:

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

Materialele de curs sunt în limba Engleză. Predarea se face în limba Română.

Citește agenda cursului
Citește agenda cursului
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.

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

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.

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

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.

Lessons
  • 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.

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

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.

Lessons
  • 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.

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
Este recomandat să continui cu:
Programe de certificare

Acest curs îi pregătește pe studenți pentru susținerea examenului de certificare Microsoft DP-201.

Detalii curs

Durată
2
zile

Preț
380
EUR

Modalități de livrare
Predare în clasă, Clasă hibridă, Clasă virtuală
Nivel de specializare
2. Associate
Într-o economie bazată pe cunoaștere, educarea și instruirea angajaților reprezintă o investiție în vederea obținerii unei performanțe îmbunătățite pe termen scurt, precum și a succesului pe termen lung al organizației.
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