Implementing an Azure AI Solution DP-200
CODE: MCS_DP-200T01
LENGTH: 24 Hours (3 days)
PRICE: CHf2’050.00
Description
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 No-SQL 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.
Audience
- 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
- Durchführung in Kooperation mit Arrow ECS Österreich und ETC
- Für Produkte, die mit Fremdwährung berechnet werden, behalten wir uns das Recht vor, die Preise bei Währungsschwankungen anzupassen.
Programme
- 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
- Lab: Azure for the Data Engineer
- Identify the evolving world of data
- Determine the Azure Data Platform Services
- Identify tasks to be performed by a Data Engineer
- Finalize the data engineering deliverables
- 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
- Lab: Working with Data Storage
- Choose a data storage approach in Azure
- Create a Storage Account
- Explain Data Lake Storage
- Upload data into Data Lake Store
- 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
- Lab: 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
- 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
- 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
- Lab: 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
- Lab: 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
- Lab: Orchestrating Data Movement with Azure Data Factory
- Explain how 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
- Lab: 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
- Lab: 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
Further Information
- Durchführung in Kooperation mit Arrow ECS Österreich und ETC
- Für Produkte, die mit Fremdwährung berechnet werden, behalten wir uns das Recht vor, die Preise bei Währungsschwankungen anzupassen.