<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Real-Time Analytics on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/real-time-analytics/</link><description>Recent content in Real-Time Analytics 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/real-time-analytics/index.xml" rel="self" type="application/rss+xml"/><item><title>Simulating Real-time Supply Chain Events with Kafka</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/02-kafka-event-simulation/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/02-kafka-event-simulation/</guid><description>&lt;h2 id="chapter-2-simulating-real-time-supply-chain-events-with-kafka"&gt;Chapter 2: Simulating Real-time Supply Chain Events with Kafka&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 2 of our comprehensive guide! In this chapter, we&amp;rsquo;re laying the foundation for our real-time supply chain analytics platform by simulating the very events that drive it. We will build a robust Kafka producer application that generates realistic supply chain events, such as shipment updates, inventory changes, and order status modifications, and publishes them to a Kafka topic.&lt;/p&gt;</description></item><item><title>Simulating Real-time Supply Chain Events with Kafka</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/02-kafka-event-simulation/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/02-kafka-event-simulation/</guid><description>&lt;h2 id="chapter-2-simulating-real-time-supply-chain-events-with-kafka"&gt;Chapter 2: Simulating Real-time Supply Chain Events with Kafka&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 2 of our comprehensive guide! In this chapter, we&amp;rsquo;re laying the foundation for our real-time supply chain analytics platform by simulating the very events that drive it. We will build a robust Kafka producer application that generates realistic supply chain events, such as shipment updates, inventory changes, and order status modifications, and publishes them to a Kafka topic.&lt;/p&gt;</description></item><item><title>Real-time Supply Chain Delay Analytics (Gold Layer)</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/05-dlt-gold-delay-analytics/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/05-dlt-gold-delay-analytics/</guid><description>&lt;h2 id="chapter-5-real-time-supply-chain-delay-analytics-gold-layer"&gt;Chapter 5: Real-time Supply Chain Delay Analytics (Gold Layer)&lt;/h2&gt;
&lt;h3 id="chapter-introduction"&gt;Chapter Introduction&lt;/h3&gt;
&lt;p&gt;Welcome to Chapter 5, where we elevate our supply chain data from the Silver layer to the Gold layer. In this crucial phase, we will build Databricks Delta Live Tables (DLT) pipelines to perform real-time aggregations and derive actionable insights for supply chain delay analytics. This involves taking the cleaned and enriched data from our Silver tables and transforming it into easily consumable metrics, such as average delay times, on-time delivery rates, and identifying critical delay incidents.&lt;/p&gt;</description></item><item><title>Real-time Supply Chain Delay Analytics (Gold Layer)</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/05-dlt-gold-delay-analytics/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/05-dlt-gold-delay-analytics/</guid><description>&lt;h2 id="chapter-5-real-time-supply-chain-delay-analytics-gold-layer"&gt;Chapter 5: Real-time Supply Chain Delay Analytics (Gold Layer)&lt;/h2&gt;
&lt;h3 id="chapter-introduction"&gt;Chapter Introduction&lt;/h3&gt;
&lt;p&gt;Welcome to Chapter 5, where we elevate our supply chain data from the Silver layer to the Gold layer. In this crucial phase, we will build Databricks Delta Live Tables (DLT) pipelines to perform real-time aggregations and derive actionable insights for supply chain delay analytics. This involves taking the cleaned and enriched data from our Silver tables and transforming it into easily consumable metrics, such as average delay times, on-time delivery rates, and identifying critical delay incidents.&lt;/p&gt;</description></item><item><title>End-to-End Real-time Procurement Price Intelligence</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/11-procurement-price-intelligence/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence-2/11-procurement-price-intelligence/</guid><description>&lt;h2 id="chapter-11-end-to-end-real-time-procurement-price-intelligence"&gt;Chapter 11: End-to-End Real-time Procurement Price Intelligence&lt;/h2&gt;
&lt;h3 id="1-chapter-introduction"&gt;1. Chapter Introduction&lt;/h3&gt;
&lt;p&gt;In this pivotal chapter, we will construct an end-to-end real-time procurement price intelligence pipeline. This pipeline is crucial for modern supply chains, enabling organizations to react swiftly to price fluctuations, optimize procurement costs, and mitigate risks associated with volatile markets. By leveraging the power of Apache Kafka for real-time event ingestion, Databricks Delta Live Tables (DLT) for robust stream processing, and Delta Lake with Unity Catalog for reliable data storage and governance, we will build a system that delivers actionable insights continuously.&lt;/p&gt;</description></item><item><title>End-to-End Real-time Procurement Price Intelligence</title><link>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/11-procurement-price-intelligence/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/realtime-supply-chain-intelligence/11-procurement-price-intelligence/</guid><description>&lt;h2 id="chapter-11-end-to-end-real-time-procurement-price-intelligence"&gt;Chapter 11: End-to-End Real-time Procurement Price Intelligence&lt;/h2&gt;
&lt;h3 id="1-chapter-introduction"&gt;1. Chapter Introduction&lt;/h3&gt;
&lt;p&gt;In this pivotal chapter, we will construct an end-to-end real-time procurement price intelligence pipeline. This pipeline is crucial for modern supply chains, enabling organizations to react swiftly to price fluctuations, optimize procurement costs, and mitigate risks associated with volatile markets. By leveraging the power of Apache Kafka for real-time event ingestion, Databricks Delta Live Tables (DLT) for robust stream processing, and Delta Lake with Unity Catalog for reliable data storage and governance, we will build a system that delivers actionable insights continuously.&lt;/p&gt;</description></item><item><title>Building a Real-time Supply Chain Intelligence Platform with Databricks Lakehouse: A Complete Production-Ready Guide</title><link>https://ai-blog.noorshomelab.dev/projects/realtime-supply-chain-intelligence-databricks-guide/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/projects/realtime-supply-chain-intelligence-databricks-guide/</guid><description>&lt;h2 id="project-overview"&gt;Project Overview&lt;/h2&gt;
&lt;p&gt;Welcome to the comprehensive guide for building a &lt;strong&gt;Real-time Supply Chain Intelligence Platform with Databricks Lakehouse&lt;/strong&gt;. In today&amp;rsquo;s volatile global economy, supply chains are constantly challenged by disruptions, fluctuating costs, and complex trade regulations. This project aims to equip developers with the skills to build a robust, scalable, and intelligent platform that provides real-time visibility and predictive analytics for critical supply chain metrics.&lt;/p&gt;
&lt;p&gt;We will construct an end-to-end data platform that ingests streaming supply chain events, performs real-time delay analytics, conducts HS (Harmonized System) Code-based import-export tariff impact analysis with historical trends, monitors logistics costs with tariff and fuel price correlation, and validates customs trade data for anomaly detection. The ultimate goal is to deliver a real-time procurement price intelligence pipeline, enabling proactive decision-making and optimizing operational efficiency.&lt;/p&gt;</description></item><item><title>Learn Redis in 2025: From Novice to Advanced Applications with Node.js &amp;amp; Python</title><link>https://ai-blog.noorshomelab.dev/guides/learn-redis-2025-guide/</link><pubDate>Fri, 07 Nov 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/guides/learn-redis-2025-guide/</guid><description>&lt;p&gt;This document is your complete roadmap to mastering Redis in 2025. Designed for absolute beginners, it will take you on a journey from understanding the very basics of what Redis is, why it&amp;rsquo;s so powerful, and how to get it running, all the way to building sophisticated, real-world applications using its advanced features. We&amp;rsquo;ll explore the latest capabilities of Redis 8.x, delve into its diverse data structures, and provide hands-on examples and guided projects using both Node.js and Python.&lt;/p&gt;</description></item></channel></rss>