🔄 Migration Guides¶
Comparative positioning note
This document is written from the perspective of Microsoft Azure, Cloud Scale Analytics, and CSA Loom. Any description of third-party or competing products, services, pricing, or capabilities is derived from publicly available documentation and sources believed accurate at the time of writing, and is provided for general comparison only. We do not claim expertise in, or authority over, any non-Microsoft product or service; the respective vendor's official documentation is the authoritative source for their offerings, which may change over time. Nothing here is intended to disparage any vendor — where a competing product has genuine advantages, we aim to note them honestly. Verify all third-party details against the vendor's current official documentation before making decisions.
Overview¶
Comprehensive guides for migrating legacy on-premises and cloud data workloads to the Cloud Scale Analytics platform on Azure.
Migration Scenarios¶
| Scenario | Description |
|---|---|
| On-premises to Azure | Migrate traditional data warehouses and ETL pipelines to Azure Synapse Analytics |
| Legacy ETL to ADF | Modernize SSIS/Informatica/Talend workloads to Azure Data Factory |
| Hadoop to Azure | Migrate Hadoop/Spark workloads to Azure Databricks or Synapse Spark |
| SQL Server to Synapse | Move analytical workloads from SQL Server to Azure Synapse dedicated pools |
Migration Framework¶
A successful migration typically follows these phases:
- Assessment: Evaluate existing workloads, dependencies, and data volumes
- Planning: Design the target architecture and create a migration roadmap
- Preparation: Set up the Azure environment and establish connectivity
- Migration: Execute data and workload migration in phases
- Validation: Verify data integrity and performance benchmarks
- Cutover: Transition production workloads and decommission legacy systems