ai_data_registry
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| ai_data_registry [2025/05/25 19:53] – [Advanced Examples] eagleeyenebula | ai_data_registry [2025/05/25 19:55] (current) – [Conclusion] eagleeyenebula | ||
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| ==== Best Practices ==== | ==== Best Practices ==== | ||
| - | To get the most out of **DataCatalog**, | + | To get the most out of **DataCatalog**, |
| - | * **Use Metadata Consistently**: | + | * **Use metadata consistently** Add fields like **source**, **size**, and **tags** to all datasets |
| - | Ensure that metadata | + | * **Secure |
| - | | + | * **Version |
| - | * **Secure | + | * **Automate |
| - | | + | |
| - | + | ||
| - | * **Version-Control Your Datasets**: | + | |
| - | Use versioning (e.g., | + | |
| - | + | ||
| - | * **Automate | + | |
| - | Integrate registry updates using pipeline automation | + | |
| - | + | ||
| - | --- | + | |
| ==== Extensibility ==== | ==== Extensibility ==== | ||
| Line 267: | Line 258: | ||
| ==== Conclusion ==== | ==== Conclusion ==== | ||
| - | The **DataCatalog** module is a scalable and flexible solution for managing metadata registries. With support for versioning, extensibility, | + | The **DataCatalog** module is a scalable and flexible solution for managing metadata registries. With support for versioning, extensibility, |
| - | + | ||
| - | Whether you’re working on small-scale or enterprise-level pipelines, the **DataCatalog** provides all the tools you need for clean and structured data management. | + | |
ai_data_registry.1748202783.txt.gz · Last modified: 2025/05/25 19:53 by eagleeyenebula
