{"id":4797,"date":"2025-09-08T13:18:33","date_gmt":"2025-09-08T11:18:33","guid":{"rendered":"https:\/\/revodata.nl\/?p=4797"},"modified":"2025-11-26T11:08:15","modified_gmt":"2025-11-26T10:08:15","slug":"from-bi-to-databricks-simplifying-architecture-layers","status":"publish","type":"post","link":"https:\/\/revodata.nl\/nl\/from-bi-to-databricks-simplifying-architecture-layers\/","title":{"rendered":"From BI to Databricks: Simplifying Architecture Layers"},"content":{"rendered":"
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From BI to Databricks: Simplifying Architecture Layers<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Over the past few weeks, we\u2019ve been exploring the journey from traditional Business Intelligence (BI) to Databricks. As part of this transition, it\u2019s essential to address a key aspect: architecture. While the terminology might seem daunting at first\u2014Bronze, Silver, Gold, these layers aren\u2019t so different from what you\u2019re already familiar with. Let\u2019s break it down and show how you can adapt this framework to suit your organization.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Layers Are Layers\u2014Let\u2019s Keep It Simple<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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When it comes to data architectures, we all think in layers. They bring structure and clarity to an otherwise complex ecosystem. So, if you\u2019re transitioning to the medallion architecture with its Bronze, Silver, and Gold layers, don\u2019t let the terminology overwhelm you. We\u2019ve even seen customers add Platinum and Diamond to their layers\u2014why not? If it works for your organization, it works! Remember, a framework is just a starting point; tailor it to fit your needs.<\/p><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Mapping Staging to the Bronze Layer<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The key is to focus on the characteristics of each layer. For example, in the MSBI world, a staging layer is where raw source data lands. It\u2019s still structured around the source, with minimal transformation. The Bronze layer in Databricks serves the same purpose: it\u2019s the raw, unprocessed representation of the source data. Once you see this connection, the transition becomes less intimidating.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Mapping the Data Warehouse to the Silver Layer<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The Data Warehouse layer in MSBI aligns closely with the Silver layer in the medallion architecture. In this stage, you introduce organizational standards, naming conventions, and other structures while keeping data at its lowest granularity. This layer is your backbone, designed to remain stable over time.<\/p><\/div>

One key difference in Databricks is the flexibility around traditional data modeling approaches like Kimball or Inmon (star-schema), Anchor modeling, or Data Vault. Here, you can choose how strictly to adhere to these techniques based on your organizational needs. However, it\u2019s critical to ensure this layer is resilient. Changes to data sources or organizational structures should have minimal impact on your models. To achieve this, consider domain-driven design, bounded contexts, and data mesh principles\u2014these sociotechnical concepts help keep your architecture flexible and future-proof.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The Data Mart Layer: Gold (or Platinum, or Diamond)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The final layer\u2014often referred to as the Gold layer in Databricks\u2014is where you optimize data for consumption. Whether it\u2019s a one-big-table design, 3NF, or star-schema, this layer is about delivering business value. Because of its direct impact on the end user, this is where companies tend to allocate the most investment. However, it\u2019s vital not to overlook the upstream layers. A stable foundation is the only way to ensure a reliable and effective Gold layer.<\/p><\/div>

At RevoData, we\u2019ve learned that a logical and user-friendly structure for your Data Catalog is key. Instead of naming catalogs \u201cBronze,\u201d \u201cSilver,\u201d or \u201cGold,\u201d we use descriptive labels like \u201csources,\u201d \u201cdomains,\u201d or \u201cdata products\u201d and apply the familiar terms as metadata tags. This approach provides a clear path to data for all users while keeping the architecture intuitive and scalable.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Make your Architecture Work for You <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Transitioning to Databricks doesn\u2019t mean starting from scratch. By mapping your existing architecture to the medallion framework and customizing it for your organization, you can create a system that\u2019s both familiar and future-ready.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Ready to Take the Next Step? <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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At RevoData, we specialize in helping organizations make the most of Databricks. Whether you\u2019re starting your journey or looking to refine your approach, we\u2019re here to support you. Let us show you how Databricks can transform your data strategy and deliver real business impact. Reach out to us today to get started!<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\tRafal Frydrych\t\t\t\t\t\t<\/h4>\n\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t
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Senior Consultant at RevoData, sharing with you his knowledge in the opinionated series: Migrating from MSBI to Databricks. <\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

From BI to Databricks: Simplifying Architecture Layers Over the past few weeks, we\u2019ve been exploring the journey from traditional Business Intelligence (BI) to Databricks. As part of this transition, it\u2019s essential to address a key aspect: architecture. While the terminology might seem daunting at first\u2014Bronze, Silver, Gold, these layers aren\u2019t so different from what you\u2019re […]<\/p>\n","protected":false},"author":2,"featured_media":4802,"comment_status":"open","ping_status":"closed","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[14,21],"tags":[],"class_list":["post-4797","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-it","category-databricks"],"_links":{"self":[{"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/posts\/4797","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/comments?post=4797"}],"version-history":[{"count":7,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/posts\/4797\/revisions"}],"predecessor-version":[{"id":6170,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/posts\/4797\/revisions\/6170"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/media\/4802"}],"wp:attachment":[{"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/media?parent=4797"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/categories?post=4797"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/tags?post=4797"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}