SQL Server , SSIS ETL & ADF and Microsoft Fabric

If you want to become a Microsoft Fabric Data Engineer/Analytics Engineer, below is a comprehensive course syllabus that covers beginner to advanced topics.

Module 1: Microsoft Fabric Fundamentals

• Introduction to Microsoft Fabric
• Microsoft Fabric Architecture
• SaaS Platform
• OneLake Overview
• Workspaces
• Capacity & Licensing
• Security & Governance
• Fabric User Interface

Module 2: OneLake

• OneLake Concepts
• Shortcuts
• Data Organization
• File Formats (CSV, Parquet, Delta)
• OneLake Explorer
• Data Sharing

Module 3: Lakehouse

• Create Lakehouse
• Bronze, Silver & Gold Architecture
• Data Loading
• SQL Endpoint
• Delta Tables
• Partitioning
• Data Optimization

Module 4: Data Factory

• Pipelines
• Copy Data Activity
• Dataflows Gen2
• Parameters
• Variables
• Triggers
• Scheduling
• Error Handling
• Monitoring Pipelines

Module 5: Apache Spark

• Spark Fundamentals
• PySpark Basics
• Spark SQL
• Notebooks
• DataFrames
• Transformations
• Performance Tuning
• Spark Job Monitoring

Module 6: Data Warehouse

• Fabric Warehouse
• SQL Development
• Views
• Stored Procedures
• CTAS
• Performance Optimization
• Warehousing Best Practices

Module 7: SQL in Fabric

• T-SQL
• Joins
• Window Functions
• CTEs
• MERGE
• Indexing
• Query Optimization
• Dynamic SQL

Module 8: Data Engineering

• ETL vs ELT
• Incremental Loading
• CDC
• Slowly Changing Dimensions (SCD)
• Data Quality
• Data Validation
• Logging Framework
• Metadata Driven Pipelines

Module 9: Power BI Integration

• Semantic Models
• DAX
• Reports
• Dashboards
• Row-Level Security
• Publishing
• Refresh
• Deployment

Module 10: Real-Time Intelligence

• Eventstream
• Eventhouse
• KQL
• Streaming Data
• Real-Time Dashboards
• Alerts
• Data Activator

Module 11: Data Science

• Notebooks
• Machine Learning
• MLflow
• Model Training
• Model Deployment

Module 12: Administration

• Workspace Administration
• Capacity Management
• Permissions
• Governance
• Monitoring
• Auditing
• Backup & Recovery

Module 13: DevOps

• Git Integration
• CI/CD
• Deployment Pipelines
• Version Control
• Environment Management

Module 14: End-to-End Project

Build a complete project:

1. Load data from SQL Server/CSV/API.
2. Store in OneLake.
3. Transform using Dataflows and Spark.
4. Build a Lakehouse.
5. Create a Data Warehouse.
6. Develop a Semantic Model.
7. Build Power BI reports.
8. Schedule automated refreshes.

Recommended prerequisites

• SQL (Intermediate)
• SSIS or ETL concepts
• Azure basics
• Power BI basics
• Python/PySpark (optional but recommended)

For someone with 12+ years of SQL Server and SSIS experience, Microsoft recommends focusing on the DP-700 (Data Engineering) course and the DP-600 (Fabric Analytics Engineer) learning path, as these align closely with enterprise data engineering and analytics roles.