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🗃️ Data Manager – Legacy Data Management Tool ​

Data Manager is a legacy platform historically used for managing data flows, metadata, and transformations within the enterprise. It has supported various operational and analytical data processes but is now being phased out in favor of modern, cloud-native solutions.

🔍 Description ​

Data Manager provided a centralized interface for designing, scheduling, and monitoring data workflows. It was often used in conjunction with on-premises databases and reporting systems, offering basic ETL capabilities and metadata tracking.

📦 Use Cases ​

  • Managing batch data transfers between internal systems
  • Scheduling and monitoring legacy data workflows
  • Maintaining metadata and transformation logic
  • Supporting reporting and operational dashboards

🧱 Architecture ​

[Internal Source Systems]
↓
[Data Manager Workflows]
↓
[Staging / Reporting Databases / Data Warehouse]

✅ Best Practices ​

  • Document all existing workflows and dependencies before decommissioning
  • Identify reusable logic for migration to modern platforms
  • Schedule jobs during low-traffic windows to reduce system impact
  • Maintain version control of workflow definitions and scripts
  • Archive historical logs and metadata for audit purposes

🔐 Governance & Access ​

  • Access managed via internal user roles and project-level permissions
  • Limited audit capabilities; manual logging often required
  • Ensure backup of all configurations and job definitions
  • Restrict access to production workflows to certified users
  • Maintain documentation of data sources, targets, and transformations

🛣️ Roadmap ​

  • Fully decommission Data Manager and migrate to Azure-native tools (ADF, External)
  • Rebuild critical workflows using CI/CD and infrastructure-as-code
  • Integrate metadata into Microsoft Purview or equivalent catalog
  • Train teams on new platforms and enforce modern governance standards
  • Establish clear ownership and lifecycle policies for migrated workflows

🧠 Data Manager has played a key role in legacy data operations, but transitioning to modern platforms will enhance scalability, automation, and governance.