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ai_data_registry

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ai_data_registry [2025/05/25 19:47] – [Basic Use Case] eagleeyenebulaai_data_registry [2025/05/25 19:55] (current) – [Conclusion] eagleeyenebula
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 You can add more intricate metadata, such as tags and related datasets, to help track high-level attributes of your datasets. You can add more intricate metadata, such as tags and related datasets, to help track high-level attributes of your datasets.
  
-<code python>+<code>
 catalog.add_entry("model_training_data", { catalog.add_entry("model_training_data", {
     "source": "AWS S3",     "source": "AWS S3",
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 === 2. Custom Storage Paths === === 2. Custom Storage Paths ===
-By default, the **DataCatalog** saves the registry in `data_registry.json`. You can configure it to use a different file path when needed.+By default, the **DataCatalog** saves the registry in **data_registry.json**. You can configure it to use a different file path when needed.
  
-<code python> 
-# Initialize the catalog with a custom file path 
-catalog = DataCatalog(registry_path="/tmp/custom_data_registry.json") 
  
-# Add an entry to the catalog+# **Initialize the catalog with a custom file path** 
 +<code> 
 +catalog = DataCatalog(registry_path="/tmp/custom_data_registry.json") 
 +</code> 
 +# **Add an entry to the catalog** 
 +<code>
 catalog.add_entry("experiment_data", { catalog.add_entry("experiment_data", {
     "source": "API",     "source": "API",
     "size": "5 GB"     "size": "5 GB"
 }) })
- +</code> 
-# Output the catalog contents+# **Output the catalog contents** 
 +<code>
 print(catalog.load_catalog()) print(catalog.load_catalog())
 </code> </code>
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 Combine **DataCatalog** with versioning capabilities. This allows you to track the progress of specific dataset versions directly in your pipeline. Combine **DataCatalog** with versioning capabilities. This allows you to track the progress of specific dataset versions directly in your pipeline.
  
-<code python>+<code>
 def versioned_entry(catalog, dataset_name, version, **kwargs): def versioned_entry(catalog, dataset_name, version, **kwargs):
     """     """
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     }     }
     catalog.add_entry(dataset_name, metadata)     catalog.add_entry(dataset_name, metadata)
- +</code> 
-# Initialize catalog+# **Initialize catalog** 
 +<code>
 catalog = DataCatalog() catalog = DataCatalog()
- +</code> 
-# Add a versioned entry+# **Add a versioned entry** 
 +<code>
 versioned_entry( versioned_entry(
     catalog,     catalog,
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     size="2 GB"     size="2 GB"
 ) )
- +</code> 
-# Output the catalog+# **Output the catalog** 
 +<code>
 print(catalog.load_catalog()) print(catalog.load_catalog())
 </code> </code>
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 **Expected Output:** **Expected Output:**
  
-<code json>+<code>
 { {
     "customer_data": {     "customer_data": {
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 ==== Best Practices ==== ==== Best Practices ====
  
-To get the most out of **DataCatalog**, consider applying the following best practices:+To get the most out of **DataCatalog**, apply these best practices:
  
-  * **Use Metadata Consistently**:   +  * **Use metadata consistently** Add fields like **source**, **size**, and **tags** to all datasets for uniformity. 
-    Ensure that metadata fields like `source`, `size`, and `tags` are added across all datasets to enable uniformity. +  * **Secure the registry file** Protect **data_registry.json** with proper file permissions to prevent unauthorized access. 
-     +  * **Version datasets** Track changes over time using clear versioning (e.g., `v1.0.0`). 
-  * **Secure Your Registry File**:   +  * **Automate updates** Use tools like **Airflow** or **Prefect** to keep the registry accurate and up to date.
-    Protect the catalog registry (`data_registry.json`) using appropriate file permissions to prevent deletion or unauthorized access. +
- +
-  * **Version-Control Your Datasets**:   +
-    Use versioning (e.g., "v1.0.0") to track iterative changes in datasets over time. +
- +
-  * **Automate Updates**:   +
-    Integrate registry updates using pipeline automation tools like **Airflow** or task orchestrators like **Prefect** to ensure accuracy. +
- +
----+
  
 ==== Extensibility ==== ==== Extensibility ====
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 ==== Conclusion ==== ==== Conclusion ====
  
-The **DataCatalog** module is a scalable and flexible solution for managing metadata registries. With support for versioning, extensibility, and pipeline integration, it ensures that complex workflows can maintain data reproducibility, traceability, and governance. +The **DataCatalog** module is a scalable and flexible solution for managing metadata registries. With support for versioning, extensibility, and pipeline integration, it ensures that complex workflows can maintain data reproducibility, traceability, and governance. 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.
- +
-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.1748202433.txt.gz · Last modified: 2025/05/25 19:47 by eagleeyenebula