Summary
Implementing an Azure Data Solution
In this course, the students will implement various data platform technologies into solutions that are in-line with business and technical requirements, including on-premises, cloud, and hybrid data scenarios incorporating both relational and NoSQL data. They will also learn how to process data using a range of technologies and languages for both streaming and batch data.
The students will also explore how to implement data security, including authentication, authorization, data policies, and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing, and streaming data solutions.
Introduction
Audience profile
The primary 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.
Prerequisites
In addition to their professional experience, students who take this training should have technical knowledge equivalent to the following courses:
– Azure Fundamentals
Outline
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Module 1: 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
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Lab : Azure for the Data Engineer
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After completing this module, students will be able to:
- 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
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Module 2: 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
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Lab : Working with Data Storage
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After completing this module, students will be able to:
- Choose a data storage approach in Azure
- Create an Azure Storage Account
- Explain Azure Data Lake Storage
- Upload data into Azure Data Lake
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Module 3: 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
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Lab : Enabling Team Based Data Science with Azure Databricks
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After completing this module, students will be able to:
- Explain Azure Databricks
- Work with Azure Databricks
- Read data with Azure Databricks
- Perform transformations with Azure Databricks
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Module 4: 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 data globally with Azure Cosmos DB
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Lab : Building Globally Distributed Databases with Cosmos DB
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After completing this module, students will be able to:
- 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 Azure Cosmos DB in Visual Studio Code
- Distribute data globally with Azure Cosmos DB
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Module 5: 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
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Lab : Working with Relational Data Stores in the Cloud
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After completing this module, students will be able to:
- Use Azure SQL Database
- Describe Azure Data Warehouse
- Create and Query an Azure SQL Data Warehouse
- Use PolyBase to Load Data into Azure SQL Data Warehouse
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Module 6: Performing Real-Time Analytics with Stream Analytics
- Explain data streams and event processing
- Data Ingestion with Event Hubs
- Processing Data with Stream Analytics Jobs
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Lab : Performing Real-Time Analytics with Stream Analytics
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After completing this module, students will:
- Be able to explain data streams and event processing
- Understand Data Ingestion with Event Hubs
- Understand Processing Data with Stream Analytics Jobs
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Module 7: Orchestrating Data Movement with Azure Data Factory
- Explain how Azure Data Factory works
- Azure Data Factory Components
- Azure Data Factory and Databricks
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Lab : Orchestrating Data Movement with Azure Data Factory
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After completing this module, students will:
- Understand Azure Data Factory and Databricks
- Understand Azure Data Factory Components
- Be able to explain how Azure Data Factory works
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Module 8: Securing Azure Data Platforms
- An introduction to security
- Key security components
- Securing Storage Accounts and Data Lake Storage
- Securing Data Stores
- Securing Streaming Data
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Lab : Securing Azure Data Platforms
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After completing this module, students will:
- Have an introduction to security
- Understand key security components
- Understand securing Storage Accounts and Data Lake Storage
- Understand securing Data Stores
- Understand securing Streaming Data
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Module 9: 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
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Lab : Monitoring and Troubleshooting Data Storage and Processing
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After completing this module, students will be able to:
- Explain the monitoring capabilities that are available
- Troubleshoot common data storage issues
- Troubleshoot common data processing issues
- Manage disaster recovery
Course Schedule
DATES
LOCATION
START
END
STATUS
**Indicates a course that is only a half day.