Microsoft Fabric Data Engineer

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.

This course, aligned with Microsoft’s DP-700 certification, provides comprehensive training on implementing data engineering solutions using Microsoft Fabric. Participants will learn to design data architectures, develop efficient data loading patterns, and orchestrate data processes. The course focuses on key areas such as data ingestion, transformation, storage, security, and monitoring, using technologies including Apache Spark, Delta Lake, Dataflows Gen2, and Fabric pipelines.

Learners will gain practical skills in real-time data processing, medallion architecture design, and querying data warehouses, along with expertise in eventstreams, KQL, and CI/CD implementation. Additionally, the course covers essential aspects of data governance, security (including row-level and column-level security), and workspace administration in Fabric environments.

Designed for experienced data professionals skilled in data extraction, transformation, and loading (ETL), the course is particularly beneficial for those familiar with SQL, PySpark, or Kusto Query Language (KQL) and cloud-based solutions like Azure. Recommended prerequisites include knowledge of relational databases, ETL workflows, data lakes, and access control mechanisms. By the end, participants will be equipped to create, deploy, and manage enterprise-scale data analytics solutions within Microsoft Fabric for real-world applications.

While there are no required prerequisites for taking this course, it is recommended that students have a foundational knowledge of core data concepts and how they’re implemented using Microsoft data services. For more information see Azure Data Fundamentals.

These following technologies and techniques will help delegates engage effectively with the course material, navigate the tools and technologies in Microsoft Fabric, and apply the learnings to real-world scenarios. These are optional but will lead to a better understating of the course.

• Familiarity with data lifecycle processes (ingestion, transformation, storage, and visualization).
• Experience working with relational databases and querying using SQL.
• Awareness of database schema concepts (e.g., tables, joins, and relationships).
• Familiarity with Extract, Transform, Load (ETL) workflows.
• Understanding the role of ETL tools in data integration.
• Awareness of cloud-based data solutions, preferably Microsoft Azure or equivalent.
• Basic knowledge of data lake and data warehouse concepts.
• Basic experience with a programming language such as Python or R (helpful for data transformation and Apache Spark modules).
• Awareness of access control mechanisms in data platforms.
• Ability to interpret business requirements and translate them into analytics use cases.

Target Audience

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).

1 - Introduction to end-to-end analytics using Microsoft Fabric

• Explore end-to-end analytics with Microsoft Fabric
• Data teams and Microsoft Fabric
• Enable and use Microsoft Fabric

2 - Get started with lakehouses in Microsoft Fabric

• Explore the Microsoft Fabric lakehouse
• Work with Microsoft Fabric lakehouses
• Explore and transform data in a lakehouse

3 - Use Apache Spark in Microsoft Fabric

• Prepare to use Apache Spark
• Run Spark code
• Work with data in a Spark dataframe
• Work with data using Spark SQL
• Visualize data in a Spark notebook

4 - Work with Delta Lake tables in Microsoft Fabric

• Understand Delta Lake
• Create delta tables
• Optimize delta tables
• Work with delta tables in Spark
• Use delta tables with streaming data

5 - Ingest Data with Dataflows Gen2 in Microsoft Fabric

• Understand Dataflows Gen2 in Microsoft Fabric
• Explore Dataflows Gen2 in Microsoft Fabric
• Integrate Dataflows Gen2 and Pipelines in Microsoft Fabric

6 - Orchestrate processes and data movement with Microsoft Fabric

• Understand pipelines
• Use the Copy Data activity
• Use pipeline templates
• Run and monitor pipelines

7 - Organize a Fabric lakehouse using medallion architecture design

• Describe medallion architecture
• Implement a medallion architecture in Fabric
• Query and report on data in your Fabric lakehouse
• Considerations for managing your lakehouse

8 - Get started with Real-Time Intelligence in Microsoft Fabric

• What is real-time data analytics?
• Real-Time Intelligence in Microsoft Fabric
• Ingest and transform real-time data
• Store and query real-time data
• Visualize real-time data
• Automate actions

9 - Use real-time eventstreams in Microsoft Fabric

• Components of eventstreams
• Eventstream sources and destinations
• Eventstream transformations

10 - Work with real-time data in a Microsoft Fabric eventhouse

• Get started with an eventhouse
• Use KQL effectively
• Materialized views and stored functions

11 - Get started with data warehouses in Microsoft Fabric

• Understand data warehouse fundamentals
• Understand data warehouses in Fabric
• Query and transform data
• Prepare data for analysis and reporting
• Secure and monitor your data warehouse

12 - Load data into a Microsoft Fabric data warehouse

• Explore data load strategies
• Use data pipelines to load a warehouse
• Load data using T-SQL
• Load and transform data with Dataflow Gen2

13 - Monitor a Microsoft Fabric data warehouse

• Monitor capacity metrics
• Monitor current activity
• Monitor queries

14 - Secure a Microsoft Fabric data warehouse

• Explore dynamic data masking
• Implement row-level security
• Implement column-level security
• Configure SQL granular permissions using T-SQL

15 - Implement continuous integration and continuous delivery (CI/CD) in Microsoft Fabric

• Understand Continuous Integration and Continuous Delivery (CI/CD)
• Implement version control and Git integration
• Implement deployment pipelines
• Automate CI/CD using Fabric APIs

16 - Monitor activities in Microsoft Fabric

• Understand monitoring
• Use Microsoft Fabric Monitor Hub
• Take action with Microsoft Fabric Activator

17 - Secure data access in Microsoft Fabric

• Understand the Fabric security model
• Configure workspace and item permissions
• Apply granular permissions

18 - Administer a Microsoft Fabric environment

• Understand the Fabric Architecture
• Understand the Fabric administrator role
• Manage Fabric security
• Govern data in Fabric