Differences

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--- ai_data_registry [2025/05/25 19:49] – [Advanced Examples] eagleeyenebula
+++ ai_data_registry [2025/05/25 19:55] (current) – [Conclusion] eagleeyenebula
@@ Line 151: / Line 151: @@
 === 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>
 # **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", {
     "source": "API",
@@ Line 164: / Line 166: @@
 </code>
 # **Output the catalog contents**
+<code>
 print(catalog.load_catalog())
 </code>
@@ Line 206: / Line 209: @@
 </code>
 # **Output the catalog**
+<code>
 print(catalog.load_catalog())
 </code>
@@ Line 230: / Line 234: @@
 ==== 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 ====
@@ Line 263: / Line 258: @@
 ==== 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.