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    <title>Databricks on Chronicles of Data Enginenering</title>
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      <title>Chronicles of Data Enginenering</title>
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      <title>📐 Semantic Layer Inside Databricks: A Look at Unity Catalog Metric Views (With an Example)</title>
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      <pubDate>Sun, 26 Jul 2026 15:06:41 +0200</pubDate>
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      <description>&lt;p&gt;&lt;img alt=&#34;semantic-view-cover&#34; loading=&#34;lazy&#34; src=&#34;https://blog.veskovujovic.me/posts/semantic-view-dbx/cover_semantic_view.png&#34;&gt;&lt;/p&gt;
&lt;h2 id=&#34;-the-enterprise-wide-enigma&#34;&gt;🤔 The enterprise-wide enigma&lt;/h2&gt;
&lt;p&gt;Ask three people on your data team what &amp;ldquo;total revenue&amp;rdquo; means and you&amp;rsquo;ll get three queries. One filters out returns. One rounds before summing. One groups by order date, another by ship date. All three are technically correct, and none of them match.&lt;/p&gt;
&lt;p&gt;This isn&amp;rsquo;t a data quality problem. The data is fine. The problem is that every dashboard, notebook, and BI tool re-implements the aggregation logic from scratch, and nothing stops those implementations from drifting apart from each other.&lt;/p&gt;</description>
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