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Course Outline

1. Azure for the Data Engineer

  • Explain the evolving landscape of data.
  • Survey services within the Azure Data Platform.
  • Identify tasks performed by a Data Engineer.
  • Describe cloud use cases through case studies.
  • Identify the evolving landscape of data.
  • Determine Azure Data Platform Services.
  • Identify tasks performed by a Data Engineer.
  • Finalize data engineering deliverables.

2. Working with Data Storage

  • Select a data storage approach in Azure.
  • Create an Azure Storage Account.
  • Explain Azure Data Lake storage.
  • Upload data into Azure Data Lake.
  • Lab: Working with Data Storage
  • Select a data storage approach in Azure.
  • Create a Storage Account.
  • Explain Data Lake Storage.
  • Upload data into Data Lake Store.

3. Enabling Team-Based Data Science with Azure Databricks

  • Explain Azure Databricks.
  • Work with Azure Databricks.
  • Read data using Azure Databricks.
  • Perform transformations using Azure Databricks.
  • Lab: Enabling Team-Based Data Science with Azure Databricks
  • Explain Azure Databricks.
  • Work with Azure Databricks.
  • Read data using Azure Databricks.
  • Perform transformations using Azure Databricks.

4. Building Globally Distributed Databases with Cosmos DB

  • Create a scalable Azure Cosmos DB database.
  • Insert and query data in your Azure Cosmos DB database.
  • Build a .NET Core app for Cosmos DB in Visual Studio Code.
  • Distribute data globally using Azure Cosmos DB.
  • Lab: Building Globally Distributed Databases with Cosmos DB
  • Create an Azure Cosmos DB.
  • Insert and query data in Azure Cosmos DB.
  • Build a .NET Core App for Azure Cosmos DB using VS Code.
  • Distribute data globally with Azure Cosmos DB.

5. Working with Relational Data Stores in the Cloud

  • Use Azure SQL Database.
  • Describe Azure SQL Data Warehouse.
  • Create and query an Azure SQL Data Warehouse.
  • Use PolyBase to load data into Azure SQL Data Warehouse.
  • Lab: Working with Relational Data Stores in the Cloud
  • Use Azure SQL Database.
  • Describe Azure SQL Data Warehouse.
  • Create and query an Azure SQL Data Warehouse.
  • Use PolyBase to load data into Azure SQL Data Warehouse.

6. Performing Real-Time Analytics with Stream Analytics

  • Explain data streams and event processing.
  • Perform data ingestion with Event Hubs.
  • Process data with Stream Analytics jobs.
  • Lab: Performing Real-Time Analytics with Stream Analytics
  • Explain data streams and event processing.
  • Perform data ingestion with Event Hubs.
  • Process data with Stream Analytics jobs.

7. Orchestrating Data Movement with Azure Data Factory

  • Explain how Azure Data Factory operates.
  • Explore Azure Data Factory components.
  • Integrate Azure Data Factory with Databricks.
  • Lab: Orchestrating Data Movement with Azure Data Factory
  • Explain how Data Factory operates.
  • Explore Azure Data Factory components.
  • Integrate Azure Data Factory with Databricks.

8. Securing Azure Data Platforms

  • Introduction to security.
  • Key security components.
  • Secure Storage Accounts and Data Lake Storage.
  • Secure Data Stores.
  • Secure streaming data.
  • Lab: Securing Azure Data Platforms
  • Introduction to security.
  • Key security components.
  • Secure Storage Accounts and Data Lake Storage.
  • Secure Data Stores.
  • Secure streaming data.

9. Monitoring and Troubleshooting Data Storage and Processing

  • Explain available monitoring capabilities.
  • Troubleshoot common data storage issues.
  • Troubleshoot common data processing issues.
  • Manage disaster recovery.
  • Lab: Monitoring and Troubleshooting Data Storage and Processing
  • Explain available monitoring capabilities.
  • Troubleshoot common data storage issues.
  • Troubleshoot common data processing issues.
  • Manage disaster recovery.

Requirements

  • Experience with basic data analysis (e.g., Excel).
  • A general understanding of cloud concepts (e.g., AWS).

Audience

  • Database engineers.
  • Developers.
 35 Hours

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