Implement Data Engineering Solutions using Microsoft Fabric
About This Course
This 4-day course equips data professionals with experience in data extraction, transformation and loading with the knowledge and hands-on skills required to implement data engineering solutions using Microsoft Fabric. Participants start with the end-to-end Fabric analytics platform, then create and load lakehouses, process data with Apache Spark and Delta Lake tables, ingest data with Dataflow Gen2, orchestrate data movement with pipelines and organise a lakehouse using the medallion architecture.
The programme then covers Real-Time Intelligence — eventstreams, eventhouses and KQL, real-time dashboards and Activator — followed by Fabric data warehouses (creating, loading, monitoring and securing them with T-SQL), and closes with continuous integration/continuous delivery (CI/CD), activity monitoring, data access security and Fabric administration. Every module includes an official Microsoft hands-on exercise performed in a live Fabric lab environment, and the course aligns to the skills measured in Microsoft Exam DP-700.
What You'll Learn
• Create a Fabric lakehouse and ingest and transform data with Apache Spark, Delta Lake tables and Dataflow Gen2. (LO2)
• Explain how pipelines orchestrate data movement and how the medallion architecture organises a lakehouse. (LO3)
• Ingest, store and query real-time data using Real-Time Intelligence, Eventstreams and an eventhouse. (LO4)
• Describe how real-time dashboards and Activator visualise and act on streaming data. (LO5)
• Create, load and analyse a data warehouse in Microsoft Fabric. (LO6)
• Explain how a Fabric data warehouse is monitored and secured. (LO7)
• Implement CI/CD deployment pipelines, monitor Fabric activities and secure data access. (LO8)
Entry Requirements
A foundational knowledge of core data concepts and how they’re implemented using Microsoft data services. For more information see Azure Data Fundamentals.
Experience with data extraction, transformation, loading, and orchestration. For more information, see Training for Data Engineers.
Experience at manipulating and transforming data with one of the following programming languages: Structured Query Language (SQL), PySpark, or Kusto Query Language (KQL).