--- title: Manufacturing data and AI: Connecting the product value... description: Databricks describes how manufacturers can connect data across the product value chain—R&D, purchasing, production, quality, sales, and field service—using its... canonical: https://daily.dev/posts/manufacturing-data-and-ai-connecting-the-product-value-chain-bbtejufow twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Manufacturing data and AI: Connecting the product value chain | daily.dev og:description: Databricks describes how manufacturers can connect data across the product value chain—R&D, purchasing, production, quality, sales, and field service—using its... og:url: https://daily.dev/posts/manufacturing-data-and-ai-connecting-the-product-value-chain-bbtejufow og:image: https://api.daily.dev/og/posts/bBTeJUFoW.png og:image:alt: Manufacturing data and AI: Connecting the product value chain og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Manufacturing data and AI: Connecting the product value chain **[databricks](https://daily.dev/sources/databricks)** · 10 min read · 0 upvotes · 0 comments ## Summary Databricks describes how manufacturers can connect data across the product value chain—R&D, purchasing, production, quality, sales, and field service—using its Data and AI Platform. It covers zero-copy Open Sharing, Lakehouse Federation, Unity Catalog governance, Lakeflow orchestration, and agentic tools like Genie One and Agent Bricks to let plant engineers and purchasing analysts query cross-system data in natural language instead of filing manual tickets. A Purchasing Genie Demo and a Mercedes-Benz Korea case study illustrate the approach, alongside an FAQ on traceability, LTAP, and streaming ingestion capabilities. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What does LTAP mean in the context of data platforms? LTAP stands for Lake Transactional/Analytical Processing, describing the ability to run both analytical and transactional workloads on one governed platform. Historically, manufacturers ran these on two separate stacks with data copied between them; LTAP consolidates both onto a single copy of governed data, supporting large-scale historical analysis alongside responsive applications that read and write operational records. _Teams weighing consolidated versus split data stacks can track platform approaches like this on daily.dev._ ### What is Lakehouse Federation and how does it avoid extra ETL pipelines? Lakehouse Federation is a Databricks capability that lets data stay in its source system while still being queried through Databricks, avoiding the need for additional ETL pipelines or data copies. Source data is governed through Unity Catalog alongside any mirrored data, giving a single governance layer regardless of where the data physically resides. _Engineers evaluating federated versus copied data architectures can follow platform capabilities like this on daily.dev._ ## Similar posts on daily.dev - [Rebuilding the data stack for AI](https://daily.dev/posts/rebuilding-the-data-stack-for-ai-8s5vdavcw) · MIT Technology Review · 1 upvotes · 0 comments - [Connecting retail demand planning to campaign and store execution](https://daily.dev/posts/connecting-retail-demand-planning-to-campaign-and-store-execution-5h54qeopd) · databricks · 1 upvotes · 0 comments - [Data Engineering in the AI Era - Taming Fragmented Agent Trace Pipelines with a Unified Lakehouse](https://daily.dev/posts/data-engineering-in-the-ai-era---taming-fragmented-agent-trace-pipelines-with-a-unified-lakehouse-62xenewdp) · Databend Blog · 0 upvotes · 0 comments - [Companies Winning with AI Built the Data Layer First](https://daily.dev/posts/companies-winning-with-ai-built-the-data-layer-first-oidgdgg1o) · databricks · 0 upvotes · 0 comments --- Tags: [#big-data](https://daily.dev/tags/big-data), [#manufacturing](https://daily.dev/tags/manufacturing), [#databricks](https://daily.dev/tags/databricks), [#unity-catalog](https://daily.dev/tags/unity-catalog) [View this post on daily.dev](https://daily.dev/posts/manufacturing-data-and-ai-connecting-the-product-value-chain-bbtejufow) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Manufacturing data and AI: Connecting the product value chain","url":"https://daily.dev/posts/manufacturing-data-and-ai-connecting-the-product-value-chain-bbtejufow","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/manufacturing-data-and-ai-connecting-the-product-value-chain-bbtejufow"},"datePublished":"2026-09-28T19:45:45.828Z","dateModified":"2026-09-28T19:46:13.330Z","description":"Databricks describes how manufacturers can connect data across the product value chain—R&D, purchasing, production, quality, sales, and field service—using its...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cc08ab9b4f353cad4d5406f57187d42f?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cc08ab9b4f353cad4d5406f57187d42f?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"databricks","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"databricks","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/fa7aa720f2db4d1eba826814730482c8","url":"https://daily.dev/sources/databricks"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/manufacturing-data-and-ai-connecting-the-product-value-chain-bbtejufow","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"big-data,manufacturing,databricks,unity-catalog","timeRequired":"PT10M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"databricks","item":"https://daily.dev/sources/databricks"},{"@type":"ListItem","position":3,"name":"Manufacturing data and AI: Connecting the product value chain"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/manufacturing-data-and-ai-connecting-the-product-value-chain-bbtejufow#faq","mainEntity":[{"@type":"Question","name":"What does LTAP mean in the context of data platforms?","acceptedAnswer":{"@type":"Answer","text":"LTAP stands for Lake Transactional/Analytical Processing, describing the ability to run both analytical and transactional workloads on one governed platform. 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