Overview
This course covers methods and practices to implement data engineering solutions by using Microsoft Fabric. Students will learn how to design and develop effective data loading patterns, data architectures, and orchestration processes.
Objectives for this course include ingesting and transforming data and securing, managing, and monitoring data engineering solutions.
This course is designed for experienced data professionals skilled at data integration and orchestration, such as those with the DP-203: Azure Data Engineer certification.
Skills Covered
- Ingest data with Microsoft Fabric
- Implement a Lakehouse with Microsoft Fabric
- Implement Real-Time Intelligence with Microsoft Fabric
- Implement a data warehouse with Microsoft Fabric
- Manage a Microsoft Fabric environment
Who Should Attend
- This audience for this course is data professionals with experience in data extraction, transformation, and loading. DP-700 is designed for professionals who need to create and deploy data engineering solutions using Microsoft Fabric for enterprise-scale data analytics.
- Learners should also have experience at manipulating and transforming data with one of the following programming languages: Structured Query Language (SQL), PySpark, or Kusto Query Language (KQL)
Course Curriculum
Course Modules
Exam & Certification
Microsoft Certified: Fabric Data Engineer Associate.
As a Fabric Data Engineer, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes.
As a candidate for this exam, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes. Your responsibilities for this role include:
- Ingesting and transforming data.
- Securing and managing an analytics solution.
- Monitoring and optimizing an analytics solution.
You work closely with analytics engineers, architects, analysts, and administrators to design and deploy data engineering solutions for analytics.
You should be skilled at manipulating and transforming data by using Structured Query Language (SQL), PySpark, and Kusto Query Language (KQL).
Skills measured
- Implement and manage an analytics solution
- Ingest and transform data
- Monitor and optimize an analytics solution
