Enterprise Data Engineering & Legacy Migration to Fabric

Instructor: Alaa Hady · Language: English or Arabic · Suggested duration: 3–4 days · Level: Advanced (Level 300)

Objectives

• Plan and execute seamless enterprise migrations from legacy data platforms (Azure Synapse Analytics, ADF, Databricks, Snowflake, SSRS/On-Prem SQL) to Microsoft Fabric.
• Engineer production-grade hybrid batch and streaming data pipelines using PySpark, T-SQL, KQL, and Eventstreams.
• Implement automated CI/CD, observability, data quality frameworks (Great Expectations / sempy), and error recovery.
• Optimize Spark and Warehouse compute performance across massive enterprise volumes.

Outline and contents

1. Assessing legacy architectures and building a phased Fabric Migration Roadmap
2. Migrating Azure Synapse Dedicated & Serverless SQL Pools and On-Prem SQL Server DWs to Fabric Data Warehouse
3. Migrating Azure Data Factory (ADF) and SSIS packages to Fabric Data Factory Pipelines and Dataflows Gen2
4. Coexistence and integration patterns with Azure Databricks and Snowflake (OneLake Shortcuts, Iceberg/Delta XTable interoperability & Database Mirroring)
5. Advanced PySpark & Delta Lake engineering: Structured Streaming, Change Data Feed (CDF), Liquid Clustering, and handling schema drift
6. Handling PII data masking, hashing, and row/column security during ingestion
7. Enterprise observability: Spark UI diagnostics, skew/shuffle tuning, Warehouse Query Insights, and automated data quality checks
8. CI/CD automation using Git, Variable Libraries, and Fabric REST APIs
9. Hands-on Migration & Engineering Lab

Target audience

Data engineers, cloud architects, Synapse/ADF/Databricks developers, and technical leads planning or executing enterprise migrations to Fabric.

Expected outcomes

Participants master advanced multi-engine data engineering in Fabric and gain actionable migration blueprints from legacy data platforms. (Note: Learners seeking dedicated exam preparation for Microsoft Exam DP-700 should also see Course 47 in Academy G).