<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bronze Layer on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/bronze-layer/</link><description>Recent content in Bronze Layer on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 20 Dec 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/bronze-layer/index.xml" rel="self" type="application/rss+xml"/><item><title>Ingesting Raw Supply Chain Events with DLT Bronze Layer</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/03-dlt-bronze-ingestion/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/03-dlt-bronze-ingestion/</guid><description>&lt;h2 id="ingesting-raw-supply-chain-events-with-dlt-bronze-layer"&gt;Ingesting Raw Supply Chain Events with DLT Bronze Layer&lt;/h2&gt;
&lt;h3 id="chapter-introduction"&gt;Chapter Introduction&lt;/h3&gt;
&lt;p&gt;In this chapter, we embark on the crucial first step of our real-time supply chain analytics journey: ingesting raw supply chain events into our data lakehouse. We will leverage Databricks Delta Live Tables (DLT) to build a robust, fault-tolerant, and scalable pipeline that continuously reads event data from Apache Kafka and lands it into a &amp;ldquo;Bronze&amp;rdquo; Delta table. The Bronze layer serves as the raw, immutable historical record of all ingested data, preserving the original state of events as they arrive.&lt;/p&gt;</description></item><item><title>Ingesting Raw Supply Chain Events with DLT Bronze Layer</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/03-dlt-bronze-ingestion/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/03-dlt-bronze-ingestion/</guid><description>&lt;h2 id="ingesting-raw-supply-chain-events-with-dlt-bronze-layer"&gt;Ingesting Raw Supply Chain Events with DLT Bronze Layer&lt;/h2&gt;
&lt;h3 id="chapter-introduction"&gt;Chapter Introduction&lt;/h3&gt;
&lt;p&gt;In this chapter, we embark on the crucial first step of our real-time supply chain analytics journey: ingesting raw supply chain events into our data lakehouse. We will leverage Databricks Delta Live Tables (DLT) to build a robust, fault-tolerant, and scalable pipeline that continuously reads event data from Apache Kafka and lands it into a &amp;ldquo;Bronze&amp;rdquo; Delta table. The Bronze layer serves as the raw, immutable historical record of all ingested data, preserving the original state of events as they arrive.&lt;/p&gt;</description></item><item><title>Refining Supply Chain Events for Delay Analytics (Silver Layer)</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/04-dlt-silver-event-refinement/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/04-dlt-silver-event-refinement/</guid><description>&lt;h2 id="chapter-4-refining-supply-chain-events-for-delay-analytics-silver-layer"&gt;Chapter 4: Refining Supply Chain Events for Delay Analytics (Silver Layer)&lt;/h2&gt;
&lt;h3 id="chapter-introduction"&gt;Chapter Introduction&lt;/h3&gt;
&lt;p&gt;Welcome to Chapter 4! In this chapter, we will elevate the raw supply chain event data ingested into our Bronze layer to a refined, clean, and structured Silver layer using Databricks Delta Live Tables (DLT). The Bronze layer, which we established in the previous chapter, serves as our landing zone for immutable raw data. Now, our focus shifts to transforming this raw data into a format suitable for downstream analytics, particularly for identifying and analyzing supply chain delays.&lt;/p&gt;</description></item><item><title>Refining Supply Chain Events for Delay Analytics (Silver Layer)</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/04-dlt-silver-event-refinement/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/04-dlt-silver-event-refinement/</guid><description>&lt;h2 id="chapter-4-refining-supply-chain-events-for-delay-analytics-silver-layer"&gt;Chapter 4: Refining Supply Chain Events for Delay Analytics (Silver Layer)&lt;/h2&gt;
&lt;h3 id="chapter-introduction"&gt;Chapter Introduction&lt;/h3&gt;
&lt;p&gt;Welcome to Chapter 4! In this chapter, we will elevate the raw supply chain event data ingested into our Bronze layer to a refined, clean, and structured Silver layer using Databricks Delta Live Tables (DLT). The Bronze layer, which we established in the previous chapter, serves as our landing zone for immutable raw data. Now, our focus shifts to transforming this raw data into a format suitable for downstream analytics, particularly for identifying and analyzing supply chain delays.&lt;/p&gt;</description></item></channel></rss>