<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Pipeline on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/data-pipeline/</link><description>Recent content in Data Pipeline on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 28 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/data-pipeline/index.xml" rel="self" type="application/rss+xml"/><item><title>Ingesting &amp;amp; Harmonizing HS Code and Tariff Data</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/06-hs-code-tariff-ingestion/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/06-hs-code-tariff-ingestion/</guid><description>&lt;h2 id="chapter-6-ingesting--harmonizing-hs-code-and-tariff-data"&gt;Chapter 6: Ingesting &amp;amp; Harmonizing HS Code and Tariff Data&lt;/h2&gt;
&lt;h3 id="chapter-introduction"&gt;Chapter Introduction&lt;/h3&gt;
&lt;p&gt;In the intricate world of global supply chains, accurate and timely information on Harmonized System (HS) codes and associated tariffs is paramount. These codes classify traded goods, determining duties, taxes, and trade policies. In this chapter, we will build a robust data pipeline using Databricks Delta Live Tables (DLT) to ingest, cleanse, and harmonize raw HS Code and tariff data into our Customs Trade Data Lakehouse.&lt;/p&gt;</description></item><item><title>Building Custom Connectors &amp;amp; Extensions</title><link>https://ai-blog.noorshomelab.dev/metadataflow-guide-2026/11-custom-connectors-extensions/</link><pubDate>Wed, 28 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/metadataflow-guide-2026/11-custom-connectors-extensions/</guid><description>&lt;h2 id="introduction-to-building-custom-connectors--extensions"&gt;Introduction to Building Custom Connectors &amp;amp; Extensions&lt;/h2&gt;
&lt;p&gt;Welcome back, data explorer! So far, you&amp;rsquo;ve learned how to harness the power of &lt;code&gt;MetaDatasetFlow&lt;/code&gt; for managing and processing your datasets using its built-in capabilities. But what happens when your data lives in a niche database, an obscure API, or requires a truly unique preprocessing step that &lt;code&gt;MetaDatasetFlow&lt;/code&gt; doesn&amp;rsquo;t natively support? That&amp;rsquo;s where the magic of custom connectors and extensions comes in!&lt;/p&gt;
&lt;p&gt;In this chapter, we&amp;rsquo;ll dive deep into &lt;code&gt;MetaDatasetFlow&lt;/code&gt;&amp;rsquo;s flexible architecture, specifically focusing on how you can extend its functionality. You&amp;rsquo;ll learn how to build your own data source connectors to integrate with virtually any data origin and create custom transformation steps to tailor data processing to your exact needs. This ability to extend the library empowers you to tackle even the most unique dataset management challenges, making &lt;code&gt;MetaDatasetFlow&lt;/code&gt; truly adaptable to your entire data ecosystem.&lt;/p&gt;</description></item></channel></rss>