Designing an Azure Data Solution - DP-201T01

Beschreibung

Dieses Training wird per 30.08.2021 von Microsoft aus dem Programm genommen und durch das neue Training DP-203T00 Data Engineering on Microsoft Azure ersetzt. 

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.
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.

expand_more chevron_right Zielgruppe

Dieses Seminar richtet sich an:
  • 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.

    expand_more chevron_right Vorkenntnisse

    Für dieses Seminar werden folgende Kenntnisse empfohlen:
    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

    expand_more chevron_right Detail-Inhalte

    • Data Platform Architecture Considerations
      • 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
    • Azure Batch Processing Reference Architectures
      • 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
    • Azure Real-Time Reference Architectures
      • 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
      • Data Platform Security Design Considerations
        • 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
    • Designing for Resiliency and Scale
      • 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
    • Design for Efficiency and Operations
      • 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

    Terminanfrage

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