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Machine Learning Engineer
1. capture data with Microsoft Fabric:
Learn how to use Microsoft Fabric to capture and orchestrate data from various sources (e.g. files, databases or web services) via dataflows, notebooks and pipelines.
2. implementing a lakehouse with Microsoft Fabric:
This module introduces the basic components of implementing a data lakehouse with Microsoft Fabric.
3. implement real-time intelligence with Microsoft Fabric:
The real-time intelligence in Microsoft Fabric allows you to analyze streaming events in real time or near real time.
4. implement a data warehouse with Microsoft Fabric:
Explore the data warehousing process and learn how to load, monitor, protect and query a warehouse in Microsoft Fabric.
5. manage a Microsoft Fabric environment:
Microsoft Fabric is a software-as-a-service platform for data analytics. Learn how to manage your environment through Continuous Integration and Continuous Deployment (CI/CD), monitoring and security.
Requirements:
- Experience with data extraction, conversion, loading and orchestration
- Basic understanding of working with Microsoft Entra ID - authentication, authorization, users and groups
- Basic experience with Microsoft Power BI - creating workspaces, developing basic reports, publishing reports to the cloud, general familiarity with the Power BI user interface
- Familiarity with Microsoft Fabric and SQL
- Capturing and transforming data
- Protect and manage an analytics solution
- Monitoring and optimizing an analysis solution
This intensive training prepares you for the exam to become a "Microsoft Certified: Fabric Data Engineer Associate" exam.
The target audience for this course is data professionals with experience in extracting, transforming and loading data. DP-700 is designed for experts who need to build and deploy data engineering solutions using Microsoft Fabric for enterprise-wide data analytics. You should also have experience manipulating and transforming data using one of the following programming languages: Structured Query Language (SQL), PySpark or Kusto Query Language (KQL).
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