DP-203: Data Engineering on Microsoft Azure

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Course Detail
  • DP-203: Data Engineering on Microsoft Azure
  • Microsoft
  • Module: Design and Implement Data Storage (15–20%)
    • Design a data storage structure to meet business requirements (relational, non-relational, and hybrid)
    • Design and create data partitioning strategies
    • Implement indexing, distribution, and sharding strategies
    • Design and implement data retention, archiving, and lifecycle management
    • Create and configure data lake and data warehouse storage solutions (Azure Data Lake Storage Gen2, Azure Synapse Analytics)
  • Module: Develop Data Processing (40–45%)
    • Ingest and transform data with Azure Data Factory, Azure Synapse pipelines, and Azure Databricks
    • Implement batch processing and streaming solutions (Event Hubs, IoT Hub, Stream Analytics)
    • Optimize data processing solutions for performance and cost efficiency
    • Integrate and transform data using Spark, SQL, and mapping data flows
    • Handle incremental data loading, schema drift, and late-arriving data
  • Module: Secure, Monitor, and Optimize Data Solutions (30–35%)
    • Implement data security (authentication, authorization, encryption)
    • Use Azure Key Vault for secrets and encryption keys
    • Configure auditing, data masking, and firewall rules for data services
    • Monitor data pipelines, storage, and compute resources using Azure Monitor and Log Analytics
    • Troubleshoot and optimize data storage and processing performance
  • Module: Design and Develop Data Solutions (Additional cross-domain skills)
    • Implement data integration patterns (Lambda, Kappa)
    • Design for disaster recovery and high availability
    • Select appropriate data processing technologies based on requirements
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