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DP-700 Microsoft Fabric Data Engineer
Modern data architectures are growing more complex – and Microsoft Fabric offers a unified response. Engineering, analytics, and governance merge in a single platform, calling for new competencies.
Key topics
- Platform concepts and their role in modern data engineering.
- Building scalable data flows and efficient architectural models.
- Connecting processing, analysis, and reporting.
- Security, cost, and performance management in Fabric.
- Field-tested methods for sustainable operations.
Prerequisite
Knowledge of SQL and relational databases is expected; experience with cloud-based data pipelines makes it easier to get started.
Target group
Suited for all who manage data processes in enterprise environments – especially data engineers, analytics engineers, and BI developers with cloud experience.
The outcome is a solid understanding of integrated data platforms that serves as a reliable foundation for complex, data-driven scenarios.
Course content
Using Dataflows Gen2 to load data
- Dataflows Gen2 within the Fabric environment
- Capabilities and features of Dataflows Gen2
- Combining Dataflows Gen2 with pipelines
Data flow and process control
- Core principles of data pipelines
- Working with the copy tool
- Applying templates effectively
- Running and validating processes
Real-time data processing in Fabric
- Foundations of real-time analytics
- Working with live data in Microsoft Fabric
- Ingesting and transforming data streams
- Real-time storage and data access
- Making results immediately available
- Automated process control
Event streams and streaming in Fabric
- Structure of real-time event streams
- Sources and destinations of data streams
- Transformations within event streams
Processing real-time data
- Configuring and running Eventhouse
- Applying KQL effectively
- Using materialized views and stored procedures
End-to-end analytics with Fabric
- Complete analytics workflows in Microsoft Fabric
- Collaboration of data teams in Fabric
- Setting up and activating Microsoft Fabric
Setting up and using lakehouses
- Overview of Microsoft Fabric
- Building and managing lakehouse structures
- Evaluating and processing data
Spark processing in Microsoft Fabric
- Configuring Apache Spark
- Writing and executing Spark code
- Processing data in Spark DataFrames
- Using Spark SQL for data queries
- Displaying results in Spark notebooks
Working with Delta Lake tables
- Understanding Delta Lake fundamentals
- Creating and maintaining delta tables
- Optimizing performance and data quality
- Combining Delta Lake with Apache Spark
- Using delta tables for streaming scenarios
Building a medallion architecture
- Concept and structure of the medallion pattern
- Implementing medallion architecture in Fabric
- Creating queries and reports in the Fabric Lakehouse
- Lakehouse management and governance
Visualizing real-time data
- Getting started with real-time dashboards
- Advanced features and configuration options
- Best practices for optimal use
Building a data warehouse
- Core concepts of the data warehouse
- Building warehouse structures in Fabric
- Querying and preparing data
- Data preparation and maintenance for analysis
- Securing and monitoring the warehouse
Importing and transforming data
- Comparing data loading methods
- Configuring pipelines for the warehouse
- Using T-SQL for data transformations
- Applying Dataflow Gen2 for loading and transforming
Querying and analyzing data
- Using the SQL editor for database queries
- Exploring the visual query editor
- Applying client tools for warehouse access
Monitoring the Microsoft Fabric Data Warehouse
- Tracking resources and capacity
- Analyzing active workloads
- Tracking requests and queries
Data security in Microsoft Fabric
- Configuring dynamic data masking
- Implementing row-level security
- Setting up column-level access restrictions
- Assigning SQL permissions precisely
CI/CD workflows in Microsoft Fabric
- CI/CD fundamentals for data platforms
- Setting up version control and Git integration
- Creating and managing deployment pipelines
- Automating CI/CD processes with Fabric APIs
Monitoring Fabric environments
- Monitoring concepts in Microsoft Fabric
- Using Monitor Hub efficiently
- Setting up activation rules with Activator
Access protection and permissions
- Security concepts in Microsoft Fabric
- Configuring access roles and permissions
- Controlling user rights precisely
Administration and governance in Fabric
- Architecture principles of Microsoft Fabric
- Roles and responsibilities of the Fabric admin
- Managing security policies centrally
- Data management and governance in Fabric
What real-world value does DP-700 certification deliver?
- Certified data engineering skills in Microsoft Fabric strengthen your ability to contribute to complex data projects and raise your professional profile.
What should you bring to the DP-700 training?
- A background in SQL, data modeling, and cloud infrastructure makes it easier to get started and significantly accelerates your learning progress.
Why is Microsoft Fabric so in demand right now?
- The ecosystem is expanding rapidly – companies are actively looking for professionals skilled in integrated data platforms and scalable analytics solutions.
Which topics are most important for the DP-700 exam?
- The focus lies on data integration, transformation, pipeline orchestration, and performance optimization within the Microsoft Fabric platform.
What sets DP-700 apart from other data engineering certifications?
- Focusing on a unified, integrated platform rather than isolated tools simplifies projects and significantly reduces coordination overhead.
What risks arise from insufficient Fabric knowledge?
- Without solid architectural knowledge, projects end up with inefficient pipelines, unnecessary costs, and unstable data quality that can jeopardize outcomes.
Who benefits most from the DP-700 training?
- Especially suited for data engineers, BI developers, and cloud specialists responsible for scalable, high-performance data solutions in Microsoft Fabric.
How soon can what you learn be applied in practice?
- Knowledge can be applied directly to ongoing projects – particularly when Microsoft Fabric is already in use within existing data infrastructure.
