DP-200 Implementing an Azure Data Solution

Agenda

• 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 and Machine Learning Platforms
• Describe the Team Data Science Process
• Provision Azure Databricks and workspaces
• Perform data preparation tasks
• 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
• Provision 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
• SQL Database and SQL Data Warehouse
• Provision an Azure SQL database to store data
• Provision and load data into Azure SQL Data Warehouse
• Performing Real-Time Analytics with Stream Analytics
• Explain data streams and event processing
• Querying streaming data using Stream Analytics
• How to process data with Azure Blob and Stream Analytics
• How to process data with Event Hubs and Stream Analytics
• Orchestrating Data Movement with Azure Data Factory
• Explain how Azure Data Factory works
• Create Linked Services and datasets
• Create pipelines and activities
• Azure Data Factory pipeline execution and triggers
• Securing Azure Data Platforms
• Configuring Network Security
• Configuring Authentication
• Configuring Authorization
• Auditing Security
• Monitoring and Troubleshooting Data Storage and Processing
• Data Engineering troubleshooting approach
• Azure Monitoring Capabilities
• Troubleshoot common data issues
• Troubleshoot common data processing issues
• Integrating and Optimizing Data Platforms
• Integrating data platforms
• Optimizing data stores
• Optimize streaming data
• Manage disaster recovery