Microsoft Fabric Analytics Engineer (DP600)

Learners will build on existing analytics experience and learn how to use Microsoft Fabric components, including lakehouses, data warehouses, notebooks, dataflows, data pipelines, and semantic models to create and deploy analytics resources.

• Module 1: Capturing data with Gen2 dataflows in Microsoft Fabric

Data collection is critical to analysis. Microsoft Fabric's Data Factory provides Dataflows (Gen2) to create and visualize multi-level data collection and transformation using Power Query Online.

• Module 2: Collecting data with Spark and Microsoft fabric notebooks

You will learn how to use Apache Spark and Python for data collection in a Microsoft Fabric Lakehouse. Fabric notebooks offer a scalable and systematic solution.

• Module 3: Using Data Factory Pipelines in Microsoft Fabric

Microsoft Fabric includes Data Factory capabilities, including the ability to create pipelines that orchestrate data collection and transformation tasks.

• Module 4: First steps with Lakehouses in Microsoft Fabric

Lakehouses combine the flexibility of data lake storage with data warehouse analytics. Microsoft Fabric is a lakehouse solution for comprehensive analyses on a single SaaS platform.

• Module 5: Organizing a Fabric Lakehouse using the Medallion Architectural Design

Explore the potential of Medallion architecture design in Microsoft Fabric. Organize and transform your data in the Bronze, Silver and Gold tiers of a Lakehouse to enable optimized analytics.

• Module 6: Using Apache Spark in Microsoft Fabric

Apache Spark is a core technology for comprehensive data analysis. Microsoft Fabric supports Spark clusters so that you can analyze and process even large amounts of data in a lakehouse.

• Module 7: Working with delta-lake tables in Microsoft Fabric

The tables in a Microsoft Fabric Lakehouse are based on the Delta Lake storage format, which is commonly used in Apache Spark. You can use the advanced functions of delta tables to create complex analysis solutions.

• Module 8: First steps with data warehouses in Microsoft Fabric

Data warehouses are analytics stores based on a relational schema to support SQL queries. With Microsoft Fabric, you can create a relational data warehouse in your workspace and easily integrate it with other elements of your end-to-end analytics solution.

• Module 9: Loading data into a Microsoft Fabric Data Warehouse

The Data Warehouse in Microsoft Fabric is a comprehensive data and analytics platform that provides advanced query processing and full transactional T-SQL capabilities for easy data management and analysis.

• Module 10: Querying a data warehouse in Microsoft Fabric

The Data Warehouse in Microsoft Fabric is a comprehensive data and analytics platform that provides advanced query processing and full transactional T-SQL capabilities for easy data management and analysis.

• Module 11: Monitoring a Microsoft Fabric Data Warehouse

A data warehouse is an important component of an enterprise analytics solution. It is important to learn how to monitor a data warehouse so that you can better understand the activity occurring within it.

• Module 12: Understanding scalability in Power BI

Scalable data models enable enterprise-wide analytics in Power BI. You will implement best practices for data modeling, use large storage formats for datasets, and practice creating a star schema to design scalable analytics solutions.

• Module 13: Creating Power BI model relationships

Power BI model relationships form the basis of a tabular model. Defining Power BI model relationships, setting up relationships, recognizing DAX relationship functions and describing the relationship evaluation.

• Module 14: Using tools to optimize Power BI performance

Develop, manage and optimize Power BI data models and DAX query performance with tools.

• Module 15: Enforcing Power BI model security

Enforce model security in Power BI using row and object level security.