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    <title>article How can I access information offline about existing features in the feature store, like feature engineering logic? in Support FAQs</title>
    <link>https://community.databricks.com/t5/support-faqs/how-can-i-access-information-offline-about-existing-features-in/ta-p/55582</link>
    <description>&lt;P&gt;&lt;SPAN&gt;The Databricks feature store provides a catalog that enables data scientists to search for existing features in the offline feature store. The feature store UI offers a searchable interface, allowing you to discover features and view the code used for to create them. You can navigate to the notebook or job containing the computation logic for a specific feature. Some of this information is consolidated into Unity Catalog.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;Databricks recommends enabling Unity Catalog integration to address the objective of resolving duplicate features or feature conflicts in the offline feature store. This integration allows for the utilization of data lineage, which can be accessed through the Data Explorer by going to &lt;STRONG&gt;Data&lt;/STRONG&gt; -&amp;gt; &lt;STRONG&gt;Data Explorer&lt;/STRONG&gt; -&amp;gt; &lt;STRONG&gt;Lineage&lt;/STRONG&gt;. Additionally, the &lt;FONT face="courier new,courier"&gt;INFORMATION_SCHEMA&lt;/FONT&gt; can be used to query catalog tables and list all the column names to identify and address naming conflicts.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;You can refer to the documentation for detailed examples and guides on utilizing the feature store APIs, best practices, and use cases:&lt;/SPAN&gt;&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;&lt;A href="https://docs.databricks.com/_extras/documents/feature-store-python-api-reference-0-10-0.pdf" target="_blank"&gt;&lt;SPAN&gt;https://docs.databricks.com/_extras/documents/feature-store-python-api-reference-0-10-0.pdf&lt;/SPAN&gt;&lt;/A&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI&gt;&lt;SPAN&gt;&lt;A href="https://www.databricks.com/resources/ebook/the-comprehensive-guide-to-feature-stores" target="_blank"&gt;https://www.databricks.com/resources/ebook/the-comprehensive-guide-to-feature-stores&lt;/A&gt;&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI&gt;&lt;A href="https://docs.databricks.com/machine-learning/feature-store/index.html" target="_blank"&gt;&lt;SPAN&gt;https://docs.databricks.com/machine-learning/feature-store/index.html&lt;/SPAN&gt;&lt;/A&gt;&lt;/LI&gt;
&lt;/UL&gt;</description>
    <pubDate>Thu, 11 Jan 2024 01:00:00 GMT</pubDate>
    <dc:creator>Adam_Pavlacka</dc:creator>
    <dc:date>2024-01-11T01:00:00Z</dc:date>
    <item>
      <title>How can I access information offline about existing features in the feature store, like feature engineering logic?</title>
      <link>https://community.databricks.com/t5/support-faqs/how-can-i-access-information-offline-about-existing-features-in/ta-p/55582</link>
      <description>&lt;P&gt;&lt;SPAN&gt;The Databricks feature store provides a catalog that enables data scientists to search for existing features in the offline feature store. The feature store UI offers a searchable interface, allowing you to discover features and view the code used for to create them. You can navigate to the notebook or job containing the computation logic for a specific feature. Some of this information is consolidated into Unity Catalog.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;Databricks recommends enabling Unity Catalog integration to address the objective of resolving duplicate features or feature conflicts in the offline feature store. This integration allows for the utilization of data lineage, which can be accessed through the Data Explorer by going to &lt;STRONG&gt;Data&lt;/STRONG&gt; -&amp;gt; &lt;STRONG&gt;Data Explorer&lt;/STRONG&gt; -&amp;gt; &lt;STRONG&gt;Lineage&lt;/STRONG&gt;. Additionally, the &lt;FONT face="courier new,courier"&gt;INFORMATION_SCHEMA&lt;/FONT&gt; can be used to query catalog tables and list all the column names to identify and address naming conflicts.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;You can refer to the documentation for detailed examples and guides on utilizing the feature store APIs, best practices, and use cases:&lt;/SPAN&gt;&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;&lt;A href="https://docs.databricks.com/_extras/documents/feature-store-python-api-reference-0-10-0.pdf" target="_blank"&gt;&lt;SPAN&gt;https://docs.databricks.com/_extras/documents/feature-store-python-api-reference-0-10-0.pdf&lt;/SPAN&gt;&lt;/A&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI&gt;&lt;SPAN&gt;&lt;A href="https://www.databricks.com/resources/ebook/the-comprehensive-guide-to-feature-stores" target="_blank"&gt;https://www.databricks.com/resources/ebook/the-comprehensive-guide-to-feature-stores&lt;/A&gt;&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI&gt;&lt;A href="https://docs.databricks.com/machine-learning/feature-store/index.html" target="_blank"&gt;&lt;SPAN&gt;https://docs.databricks.com/machine-learning/feature-store/index.html&lt;/SPAN&gt;&lt;/A&gt;&lt;/LI&gt;
&lt;/UL&gt;</description>
      <pubDate>Thu, 11 Jan 2024 01:00:00 GMT</pubDate>
      <guid>https://community.databricks.com/t5/support-faqs/how-can-i-access-information-offline-about-existing-features-in/ta-p/55582</guid>
      <dc:creator>Adam_Pavlacka</dc:creator>
      <dc:date>2024-01-11T01:00:00Z</dc:date>
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