In the world of modern data systems and platforms—especially those operating at scale, like Microsoft Dataverse, Azure Synapse, or cloud-based data warehouses—the terms managed and unmanaged tables are becoming increasingly relevant. These two types of tables represent fundamentally different approaches to data ownership, control, and lifecycle management, and choosing between them is a critical design decision in enterprise-grade solutions.
This article dives deep into the differences between managed and unmanaged tables, the pros and cons of each, and when you should use one over the other.
What Are Managed Tables?
Managed tables are tables whose data lifecycle is fully controlled by the platform or data service. When you create a managed table, the system automatically handles where and how the data is stored. It abstracts away the storage implementation, offering a simplified interface for users and developers.
In Microsoft Dataverse
In Dataverse, a managed table is typically part of a managed solution. These are components that are deployed in a packaged format, often by third-party developers or ISVs (Independent Software Vendors). The metadata and schema are tightly controlled and cannot be freely modified once deployed.
In platforms like Azure Synapse or Hive, a managed table means:
- The system stores the data files in a default location (like a data lake or HDFS).
- If you drop the table, the data is also deleted.
- The platform is responsible for optimizing storage, indexing, and metadata handling.
What Are Unmanaged Tables?
Unmanaged tables, also known as external tables, give you more control. These tables reference data that exists outside the platform’s managed storage system, such as files in Azure Data Lake Storage (ADLS), AWS S3, or an on-premise SQL server.
In Dataverse, unmanaged tables are part of unmanaged solutions—solutions that are often used during development. Developers have full control over schema, data types, relationships, and behavior. However, this flexibility comes with potential risks.
In cloud platforms, an unmanaged table:
- Does not own the data; it merely points to it.
- If the table is dropped, the underlying data remains.
- You are responsible for managing the location, lifecycle, and access to the data.
Key Differences Between Managed and Unmanaged Tables
| Feature / Characteristic | Managed Tables | Unmanaged Tables |
|---|---|---|
| Data Ownership | Platform/service owns and manages data | Data is external; user manages data |
| Schema Modification | Restricted (especially in Dataverse managed solutions) | Full control (Dataverse unmanaged or external tables) |
| Lifecycle Management | Dropping table deletes data | Dropping table leaves data untouched |
| Ease of Use | Easier for non-technical users | Requires more advanced data management knowledge |
| Security and Governance | Tied to platform-level security models | Requires external access controls |
| Portability | Harder to migrate out of platform | Easier to connect and share across systems |
| Performance Optimization | Optimized by platform (e.g., indexing, partitions) | Depends on underlying data source |
Managed Tables in Dataverse: Deep Dive
In Microsoft Dataverse, managed tables are part of a managed solution that is typically deployed to a production environment. Here are some key characteristics:
✅ Benefits:
- Governance: Changes are tracked and protected.
- Security: Integrated into the platform’s role-based access control.
- Stability: Ideal for production-ready applications.
Limitations:
- Locked Down: You can’t delete columns, change relationships, or modify schema directly.
- Dependency Management: Removing or modifying dependencies often requires removing the entire solution.
- Customization Limits: You may be forced to extend the table instead of changing it.
This is ideal for scenarios where you’re consuming a packaged app or module built by someone else—think of it like deploying an app from an app store.
Unmanaged Tables in Dataverse: Deep Dive
Unmanaged tables are more flexible and are usually used in development environments.
✅ Benefits:
- Full Control: Developers can change anything.
- Easier Iteration: Add/remove fields, tweak relationships, or adjust settings at will.
- Ideal for Prototyping: Perfect for testing and sandboxing ideas.
Risks:
- No Source Control: Without ALM practices, it’s easy to lose track of changes.
- Security Misconfigurations: Developers may not apply best-practice governance.
- Not Production-Ready: Unmanaged tables are not considered stable for long-term use.
When migrating to production, it’s common to convert your unmanaged development work into a managed solution with tested components.
Managed vs Unmanaged in Big Data Platforms
Outside of Dataverse—in platforms like Azure Synapse, Databricks, or Hive—the managed/unmanaged terminology refers to data ownership and location, rather than solution packaging.
Example: Azure Synapse Analytics
- Managed Table: Stores data in
/synapse/workspaces/warehouse/db/table. - Unmanaged Table: Points to
/datalake/raw/sales.csv.
✅ Managed Table Pros:
- Fast provisioning, tight integration with workspace tools.
- Automatic cleanup when dropping tables.
- Great for structured, well-defined datasets.
Managed Table Cons:
- Data is tied to the platform. Harder to share externally.
- May cost more due to duplicated storage.
✅ Unmanaged Table Pros:
- Perfect for analytics across shared or multi-tenant environments.
- Supports diverse file formats (Parquet, CSV, JSON).
- Can be versioned externally (Git + Lakehouse pattern).
Unmanaged Table Cons:
- You need to manage schema evolution and partitioning manually.
- Requires more discipline in access control and lifecycle management.
When to Use Managed Tables
- You’re building a production-ready app where governance and consistency are key.
- You want the platform to handle storage, optimization, and cleanup.
- You’re consuming a solution from an ISV or partner (Dataverse).
- You need to maintain tight integration with platform features (like workflows, Power Apps, or security roles).
When to Use Unmanaged Tables
- You’re in the development phase or building a proof of concept.
- You want to retain control over schema and data access.
- You’re working with external data lakes or multi-system pipelines.
- You need to customize the solution frequently or work in a flexible environment.
Real-World Example: Sales Reporting App
Imagine you’re building a Sales Reporting App with the following requirements:
- Store customer data, orders, and product lists.
- Allow analysts to query from Power BI.
- Integrate with external ERP and CRM systems.
Approach 1: Fully Managed (Dataverse)
- Use managed tables for
Customer,Order,Product. - Package the app and deploy to production via managed solution.
- Benefits: clean governance, good for end-users, works well with Power Apps.
Approach 2: Hybrid with External Data (Synapse + ADLS)
- Use unmanaged tables for data sourced from SAP and Salesforce (e.g.,
/lake/integration/crm/accounts.parquet). - Load the data into a curated managed warehouse for reporting.
- Benefits: flexible ingestion + optimized reporting layer.
This hybrid model leverages the strengths of both approaches.