Microsoft Fabric Analytics Engineer

To benefit fully from the course, delegates should possess the following foundational knowledge and skills:

Data Analytics Fundamentals:

• Basic understanding of data analysis concepts and methodologies.
• Familiarity with data lifecycle processes (ingestion, transformation, storage, and visualization).

Database Knowledge:

• Experience working with relational databases and querying using SQL.
• Awareness of database schema concepts (e.g., tables, joins, and relationships).

ETL Processes:

• Familiarity with Extract, Transform, Load (ETL) workflows.
• Understanding the role of ETL tools in data integration.

Cloud Data Platforms:

• Awareness of cloud-based data solutions, preferably Microsoft Azure or equivalent.
• Basic knowledge of data lake and data warehouse concepts.

Power BI Basics:

• Exposure to Power BI for creating dashboards and reports.
• Understanding measures, calculated columns, and relationships in Power BI.

Programming Concepts:

• Basic experience with a programming language such as Python or R (helpful for data transformation and Apache Spark modules).
• Understanding of DAX (Data Analysis Expressions) and its application in Power BI.

Security and Permissions:

• Familiarity with data security principles such as row-level and column-level security.
• Awareness of access control mechanisms in data platforms.

Business Context:

• Ability to interpret business requirements and translate them into analytics use cases.

These prerequisites 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.

Module 1: Explore end-to-end analytics with Microsoft Fabric

• Describe end-to-end analytics in Microsoft Fabric.
• Understand data teams and roles that use Fabric.
• Describe how to enable and use Fabric.

Module 2: Get started with lakehouses in Microsoft Fabric

• What is a lakehouse?
• Load data into a lakehouse
• Explore, transform, and visualize data in the lakehouse

Module 3: Ingest data with Dataflow Gen2 in Microsoft Fabric

• Understand Dataflow Gen2
• Dataflow Gen2 benefits and limitation
• Explore Dataflow Gen2
• Integrate Dataflow Gen2 and pipelines

Module 4: Orchestrate processes and data movement with Microsoft Fabric

• Pipelines in Microsoft Fabric
• Common Activities – copy data
• Use templates for common activities
• Run and monitor pipelines

Module 5: Use Apache Spark in Microsoft Fabric

• What is Apache Spark?
• How to use Apache Spark
• Prepare to use Apache Spark
• Run Spark in Fabric
• Load data in a Spark Dataframe
• Transform data in a Spark Dataframe
• Partition the output file
• Visualize data

Module 6: Get started with data warehouses in Microsoft Fabric

• Data warehouse fundamentals
• Understand Fabric warehouses
• Create a data warehouse in Fabric
• Design a data warehouse
• Data warehouse schema design
• Query data
• Visualize queries
• Build relationships
• Security overview, workspace and item permissions

Module 7: Load data into a Microsoft Fabric data warehouse

• Understand ETL (Extract, Transform and Load)
• Load dimension tables
• Load data with Fabric pipelines
• Configure the copy data assistant
• Load data with Dataflows Gen2
• Transform data with Copilot

Module 8: Query a data warehouse in Microsoft Fabric

• Aggregate measures by dimension attributes
• Joins in a snowflake schema
• Run queries and export results
• Explore visual query editor
• Connect with SQL clients
• DAX calculations
• Join in a snowflake schema

Module 9: Monitor a Microsoft Fabric data warehouse

• Monitor capacity metrics
• Warehouse operation categories
• Timepoint explore graph
• Usage considerations

Module 10: Secure a Microsoft Fabric data warehouse

• Dynamic Data Masking
• Row-level security
• Column-level security
• Granular permissions using T-SQL

Module 11: Add measures to Power BI semantic models

Understand implicit and explicit measures.

Create measures, calculated columns, and calculated tables.

Identify when to use a measure or calculated column.

Module 12: Design scalable semantic models

Choose appropriate storage modes for your semantic model.

Enable large semantic model storage format and incremental refresh.

Create relationships between tables in a semantic model.

Design dynamic elements to extend calculations in a semantic model.

Module 13: Optimize a model for performance in Power BI

• Review the performance of measures, relationships, and visuals.
• Improve performance by reducing cardinality levels.
• Optimize DirectQuery models with table level storage.
• Create and manage aggregations.

Module 14: Create and manage Power BI assets

• Create core and specialized semantic models.
• Create Power BI Template and Power BI Project files.
• Use lineage view and endorse data assets in Power BI service.
• Use XMLA endpoint to connect semantic models.

Module 15: Enforce semantic model security

• Restrict access to Power BI model data with RLS.
• Restrict access to Power BI model objects with OLS.
• Apply good development practices to enforce Power BI model security.

Module 16: Get started with Real-Time Intelligence with Microsoft Fabric

• Use basic syntax and of Kusto Query Language (KQL) and querysets.
• Describe how to execute T-SQL queries in the Queryset canvas.
• Copy, export, and share data results.

Module 17: Secure data access in Microsoft Fabric

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

Module 18: Administer Microsoft Fabric

• Describe Fabric admin tasks
• Navigate the admin center
• Manage user access