Microsoft Certified Azure Data Engineer Associate

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Duración del curso
54 horas
Lugar de impartición
Madrid / Online
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Modalidad
Aula Virtual
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Fecha de inicio
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Acerca del curso

Los ingenieros de datos Azure diseñan e implementan la gestión, la monitorización, la seguridad y privacidad de los datos mediante la pila completa de los servicios Azure para satisfacer las necesidades comerciales.

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Obtener los conocimientos necesarios para prepararse los siguientes exámenes de certificación:

  • DP-200: Implementing an Azure Data Solution.
  • DP-201: Designing an Azure Data Solution

DP-200T01: Implementing an Azure Data Solution

  • Azure for the Data Engineer
    • 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
  • Working with Data Storage
    • Choose a data storage approach in Azure
    • Create an Azure Storage Account
    • Explain Azure Data Lake storage
    • Upload data into Azure Data Lake
  • Enabling Team Based Data Science with Azure Databricks
    • Explain Azure Databricks
    • Work with Azure Databricks
    • Read data with Azure Databricks
    • Perform transformations with Azure Databricks
  • Building Globally Distributed Databases with Cosmos DB
    • 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 Cosmos DB in Visual Studio Code
    • Distribute your data globally with Azure Cosmos DB
  • Working with Relational Data Stores in the Cloud
    • Use Azure SQL Database
    • Describe Azure SQL Data Warehouse
    • Creating and Querying an Azure SQL Data Warehouse
    • Use PolyBase to Load Data into Azure SQL Data Warehouse
  • Performing Real-Time Analytics with Stream Analytics
    • Explain data streams and event processing
    • Data Ingestion with Event Hubs
    • Processing Data with Stream Analytics Jobs
  • Orchestrating Data Movement with Azure Data Factory
    • Explain how Azure Data Factory works
    • Azure Data Factory Components
    • Azure Data Factory and Databricks
  • Securing Azure Data Platforms
    • An introduction to security
    • Key security components
    • Securing Storage Accounts and Data Lake Storage
    • Securing Data Stores
    • Securing Streaming Data
  • Monitoring and Troubleshooting Data Storage and Processing
    • Explain the monitoring capabilities that are available
    • Troubleshoot common data storage issues
    • Troubleshoot common data processing issues
    • Manage disaster recovery

DP-201T01: Microsoft Certified: Azure Data Engineer Associate

  • 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
  • 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
  • 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
  • Data Platform Security Design Considerations
    • Defense in Depth Security Approach
    • Identity Management
    • Infrastructure Protection
    • Encryption Usage
    • Network Level Protection
    • Application Security
  • 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
  • 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

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