{"id":97,"date":"2025-02-06T11:01:11","date_gmt":"2025-02-06T14:01:11","guid":{"rendered":"https:\/\/alanavelar.com\/?p=97"},"modified":"2025-02-06T11:39:24","modified_gmt":"2025-02-06T14:39:24","slug":"seu-pipeline-de-dados-esta-um-caos-a-arquitetura-medallion-pode-resolver","status":"publish","type":"post","link":"https:\/\/alanavelar.com\/en\/seu-pipeline-de-dados-esta-um-caos-a-arquitetura-medallion-pode-resolver\/","title":{"rendered":"Struggling with a messy data pipeline? Medallion Architecture to the rescue!"},"content":{"rendered":"

If there's one thing I've learned from working with data architecture<\/strong>, it's that a messy pipeline becomes a time bomb<\/strong>. One day everything works; the next you find yourself hunting for a phantom error<\/strong> between layers that are more intertwined than they should be. That's where Medallion Architecture comes in<\/strong>\u2014a model that helps better structure this flow and prevent chaos.<\/p>\n\n\n\n

What is Medallion Architecture?<\/h3>\n\n\n\n

Medallion Architecture is a layered data organization model<\/strong>, designed to ensure greater governance, quality and scalability<\/strong> in information processing. It is divided into three main levels:<\/p>\n\n\n\n

    \n
  • Bronze<\/strong> \ud83e\udd49: where raw data is stored exactly as it arrives from the source.<\/li>\n\n\n\n
  • Silver<\/strong> \ud83e\udd48: where data is cleaned, structured and prepared for more advanced analysis.<\/li>\n\n\n\n
  • Gold tables<\/strong> \ud83e\udd47: where the data has undergone all necessary transformations and is ready for direct consumption in reports and dashboards.<\/li>\n<\/ul>\n\n\n\n

    This model prevents disorganized dependencies<\/strong> and ensures that each layer has a well defined purpose<\/strong>, facilitating the maintenance <\/strong>of the pipeline and the reliability <\/strong>of the data.<\/p>\n\n\n\n

    The right flow of data promotion<\/h3>\n\n\n\n

    The idea is simple: <\/strong>each layer should consume only its own data or that of the layer immediately below. It may seem like a minor rule<\/strong>, but it makes a huge difference in pipeline maintenance and reliability<\/strong>.<\/p>\n\n\n\n

    In practice, this means:<\/p>\n\n\n\n

      \n
    • Bronze<\/strong> \ud83e\udd49: Raw data, 1:1 <\/strong>with the original source, stored in a bucket\/blob<\/strong>, optionally (but preferably) partitioned.<\/li>\n\n\n\n
    • Silver<\/strong> \ud83e\udd48: Data coming from Bronze<\/strong>, but organized, stored in the database, maintaining the same number of records<\/strong>, but with structural adjustments (types, columns, standardization)<\/strong>. Depending on the need, Silver <\/strong>can also originate from another Silver table<\/strong>, but in extremely specific contexts, since any transformations shoud be applied in Gold tables<\/strong>.<\/li>\n\n\n\n
    • Gold tables<\/strong> \ud83e\udd47: Data coming from Silver <\/strong>ready for consumption, whether in modeling DW or One Big Table<\/strong> (we'll talk about this little guy soon<\/em><\/strong>), where business rules<\/strong> are applied and the number of records can be reduced or increased. It is very common<\/strong> for Gold tables <\/strong>to originate from a junction of other Gold tables<\/strong>, allowing the reuse of concepts and business rules, forming single sources of truth<\/strong>.<\/li>\n<\/ul>\n\n\n\n

      This flow ensures that transformations stay in the right place<\/strong>. If Gold tables <\/strong>needs data, it gets it from Silver<\/strong>\u2014no pulling directly from Bronze <\/strong>and mixing everything together. This prevents a chaotic pipeline where each layer has complex and unmaintainable dependencies.<\/p>\n\n\n\n

      Cross-layer validation: the idea of the \u201chandshake\u201d<\/h3>\n\n\n\n

      I like to think that each layer makes a \u201chandshake<\/em><\/strong>\u201d with the previous one. That is, before promoting a dataset<\/strong>, it must be ensured that it meets the premises of that layer<\/strong>.<\/p>\n\n\n\n

      If Bronze <\/strong>says it will store the raw data, the structuring of Silver <\/strong>relies on this and only performs the structuring and moves the data<\/strong>. If Gold tables <\/strong>applies business rules, it needs to trust that Silver <\/strong>has maintained data integrity<\/strong>. This creates a reliable pipeline<\/strong>, where each stage has a well-defined role<\/strong> and no one crosses another's line.<\/p>\n\n\n\n

      Why does this matter?<\/h3>\n\n\n\n

      Because an uncontrolled pipeline becomes an untamable monster<\/strong>. I've seen situations where Gold tables <\/strong>pulled data directly from Bronze, ignored Silver <\/strong>and applied untraceable transformations. Result? A mistake somewhere and you never know where to start looking<\/strong>.<\/p>\n\n\n\n

      When the layers respect the promotion rules, everything becomes more predictable and scalable<\/strong>. If there's a problem, you know exactly where to look. If you need to adjust something, the impact is contained within the right layer<\/strong>.<\/p>\n\n\n\n

      Conclusion<\/h3>\n\n\n\n

      Keeping an organized flow is the difference between a robust data pipeline<\/strong> and absolute chaos<\/strong>. Ensuring that each layer respects its premises is a simple but extremely effective way to avoid headaches<\/strong> down the road.<\/p>\n\n\n\n

      And you, have you ever lost nights of sleep because of a ghostly problem<\/strong> in your pipeline? Tell me in the comments!<\/p>","protected":false},"excerpt":{"rendered":"

      If there's one thing I've learned working with data architecture, it's that a messy pipeline becomes a ticking time bomb. One day everything is working, the next you're hunting for a phantom error between layers that overlap more than they should. Enter Medallion Architecture, a model that helps better structure this flow [\u2026]<\/p>","protected":false},"author":1,"featured_media":103,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9,10],"class_list":["post-97","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-arquitetura","tag-medallion"],"yoast_head":"\nSeu pipeline de dados est\u00e1 um caos? A Arquitetura Medallion pode resolver! - Alan Avelar<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/alanavelar.com\/en\/seu-pipeline-de-dados-esta-um-caos-a-arquitetura-medallion-pode-resolver\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Seu pipeline de dados est\u00e1 um caos? A Arquitetura Medallion pode resolver! - Alan Avelar\" \/>\n<meta property=\"og:description\" content=\"Se tem uma coisa que eu aprendi trabalhando com arquitetura de dados, \u00e9 que pipeline bagun\u00e7ado vira uma bomba-rel\u00f3gio. Um dia tudo est\u00e1 funcionando, no outro voc\u00ea est\u00e1 ca\u00e7ando um erro fantasma entre camadas que se misturam mais do que deveriam. 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