fact_check When to Use Databooks

account_tree Master Data Management with Databooks

Databooks are designed to act as a lightweight Master Data Management (MDM) layer on top of your existing systems, focused on the slow-changing reference data that drives consistent reporting and analysis. Instead of scattering dimension tables and business-critical lists across spreadsheets, individual BI models, and line-of-business apps, Databooks give you a governed, single source of truth that flows directly into Microsoft Fabric and Power BI.

category Typical MDM Use Cases

Typical MDM use cases for Databooks include:

• Maintaining standard lists such as chart of accounts, product catalogs, customer segments, cost centers, and regions.
• Managing custom business hierarchies, for example rolling individual products into families, or local cost centers into global departments, that need to stay in sync across many reports.
• Harmonizing codes and labels coming from multiple source systems so that the same thing looks the same everywhere in your analytics layer.
• Driving governed picklists and reference values used in downstream planning, forecasting, and operational reports so teams are always choosing from controlled options, not free-text fields.

policy Core MDM Principles in Databooks

Databooks follow general MDM principles while keeping the experience approachable for business users:

• Single source of truth: Each Databook represents a master list or hierarchy that downstream models treat as authoritative, reducing duplication and conflicting versions.
• Governance and security: Role-based permissions, row-level controls, and auditability ensure that only the right people can make changes to master data, and that those changes are traceable.
• Change without breakage: Keys and relationships remain stable even when names or groupings change, so you can safely clean up labels and hierarchies without breaking reports.
• Reuse everywhere: Once a Databook is connected, the same curated data can be reused across many datasets, semantic models, and workspaces without rework.

schedule Designed for Slow-Changing Data, Not Logs

Databooks are intentionally optimized for slow-changing, business-defined data, not for high-volume transactional or log data:

• Ideal data: Dimensions and reference tables that change infrequently, daily, weekly, or monthly, such as organizational structures, product attributes, status codes, or mapping tables between systems.
• Not for logs or events: Do not use Databooks for application logs, clickstream events, IoT feeds, detailed transaction history, or any data that arrives in large volumes and updates continuously. Those workloads belong in your data lake, warehouse, or event streaming layer, where they can be modeled and then joined to Databooks as needed.
• Intentional friction: Because Databooks are about data quality and governance, they favor clarity and control over raw throughput. If you need to append millions of rows per hour, Databooks are not the right tool.

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