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Tag Archives: medallion-architecture

L’évolution des entrepôts de données en 2025/2026

21 Sunday Dec 2025

Posted by Martyn Jones in Inform, educate and entertain.

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AI, almacen-de-datos, Analytics, Artificial Intelligence, Bill Inmon, blog, cloud, data mesh, Data Warehouse, data-lakehouse, espanol, francais, france, french, ia, Inmon, inteligencia-artificial, italiano, libri, llm, medallion-architecture, non-classe, senza-categoria, technology


Martyn Rhisiart Jones

Segovia 20th December 2025

L’entrepôt de données est mort. Vive l’entrepôt de données !

En 1992, Bill Inmon a inventé le terme « entrepôt de données » et a défini quatre règles d’or : orienté sujet, intégré, non volatil et temporel. C’était le modèle d’une forteresse de vérité, coûteuse, sur site, fonctionnant par lots et absolument indispensable. Trente ans plus tard, cette forteresse a laissé place à une plateforme cloud hyperscale. Cette plateforme peut exécuter simultanément vos modèles d’IA et le tableau de bord de votre PDG. Bienvenue dans l’entreposage de données de 2025.

La transformation a été radicale. Ce qui était autrefois des baies de serveurs bourdonnant dans un sous-sol est désormais une plateforme sans serveur et entièrement gérée. Elle fait évoluer la puissance de calcul et le stockage indépendamment. Ces plateformes ne vous facturent que ce que vous utilisez réellement. Elles vous permettent d’ingérer des pétaoctets de données en flux continu sans effort. Snowflake, Google BigQuery, Amazon Redshift, Microsoft Fabric : voici les nouveaux acteurs du marché. Ils redéfinissent les règles. Pendant ce temps, les anciennes versions sont discrètement préservées.

Le volume des données a explosé. La moitié des données est au format JSON ou Parquet semi-structuré. L’autre moitié est de plus en plus non structurée et nécessite impérativement des représentations vectorielles. Les dirigeants n’attendent plus le lendemain matin pour connaître les chiffres de la veille ; ils veulent des informations exploitables avant même que le café ne soit froid. Le cloud computing a transformé l’infrastructure à coût fixe en un service modulable, à la demande.

Il en résulte un entrepôt de données moderne. Son apparence diffère sensiblement de la vision initiale d’Inmon. Cependant, il respecte toujours ses principes fondamentaux.

Il reste orienté sujet. Les données sont organisées autour de domaines métiers tels que les clients, les ventes et les stocks. Cette organisation utilise des schémas en étoile, en flocon de neige ou des couches sémantiques. Ces couches sont construites avec des outils comme dbt et Looker.

Il reste intégré. Les pipelines ETL/ELT, les catalogues de données et les cadres de gouvernance garantissent que les sources disparates convergent vers une source unique de vérité. Snowflake Horizon et BigQuery Data Catalogue en sont des exemples.

Les données restent dépendantes du temps. Les tables horodatées et les dimensions à évolution lente de type 2 en sont des exemples. Des fonctionnalités comme TIME TRAVEL de Snowflake ou SYSTEM_TIME de BigQuery permettent de se demander : « Que savions-nous le 15 mars ?»

Et elles restent non volatiles, du moins en grande partie. Les transactions ACID permettent désormais des mises à jour et des suppressions contrôlées (coucou, RGPD !). Cependant, le noyau analytique reste en mode ajout uniquement ou avec suivi des modifications. L’historique est préservé avec la même rigueur qu’auparavant. Ce sont les adaptations qui font la différence. Le streaming en temps réel (connecteurs Kafka, Snowflake Streams, BigQuery Streaming) remplace les traitements par lots nocturnes. L’apprentissage automatique et la recherche vectorielle intégrés à la base de données alimentent les applications d’IA sans déplacer les données. Les couches sémantiques en libre-service permettent aux utilisateurs métier d’explorer les données sans avoir à solliciter le service informatique pour un nouveau rapport. Le stockage et le calcul sont découplés. On peut stocker des exaoctets à moindre coût. On peut exécuter des milliers de requêtes simultanées sans revoir le budget.

Alors, l’entrepôt de données moderne respecte-t-il toujours les principes d’Inmon ? Oui. Sans aucun doute. L’esprit perdure, même si le système a été entièrement repensé.

Le hic, c’est que cette évolution rend le choix de la plateforme plus crucial que jamais. Un mauvais choix vous rendra dépendant d’un seul fournisseur de cloud. Vous risquez alors de faire face à des coûts imprévisibles. Vous pourriez également vous retrouver avec un système incapable de gérer les charges de travail d’IA de demain. En choisissant la bonne plateforme, vous bénéficiez de performances de niveau entreprise. Elle offre une gouvernance à toute épreuve et une tarification prévisible. Elle reste accessible à tous, des ingénieurs de données à l’équipe marketing.

En 2025/2026, l’entrepôt de données n’est pas mort. Il renaît simplement sous une forme plus rapide, moins chère et bien plus puissante qu’Inmon n’aurait pu l’imaginer. Les anciennes règles restent valables. Les nouvelles sont simplement beaucoup plus intéressantes.

Merci de votre lecture.

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Evoluzione del Data Warehouse: Da Inmon al Cloud 2025/2026

21 Sunday Dec 2025

Posted by Martyn Jones in Inform, educate and entertain.

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AI, almacen-de-datos, Analytics, Artificial Intelligence, Bill Inmon, blog, cloud, data mesh, Data Warehouse, data-lakehouse, espanol, ia, Inmon, inteligencia-artificial, italiano, libri, llm, medallion-architecture, senza-categoria, technology


Martyn Rhisiart Jones

Segovia 20th December 2025

Il Data Warehouse è morto. Lunga vita al Data Warehouse.

Nel 1992, Bill Inmon coniò il termine “data warehouse”. Definì quattro regole sacre: orientato al soggetto, integrato, non volatile, e variabile nel tempo. Era il modello per una fortezza della verità, costosa, on-premise, elaborata in batch e assolutamente indispensabile. Facciamo un salto in avanti di tre decenni. La fortezza è stata sostituita da qualcosa che assomiglia a una piattaforma cloud iperscalabile. Questa piattaforma può gestire contemporaneamente i tuoi modelli di intelligenza artificiale e la dashboard del tuo CEO. Benvenuti al data warehouse nel 2025.

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El Nuevo Almacén de Datos: Evolución y Revolución

21 Sunday Dec 2025

Posted by Martyn Jones in Inform, educate and entertain.

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AI, almacen-de-datos, Analytics, Artificial Intelligence, Bill Inmon, blog, cloud, data mesh, Data Warehouse, data-lakehouse, espanol, ia, inteligencia-artificial, llm, medallion-architecture, technology


Martyn Rhisiart Jones

Segovia 20th December 2025

El almacén de datos ha muerto. ¡Viva el almacén de datos!

En 1992, Bill Inmon acuñó el término “almacén de datos” y estableció cuatro reglas sagradas: orientado al sujeto, integrado, no volátil y variable en el tiempo. Era el modelo para una fortaleza de la verdad, costosa, local, con procesamiento por lotes y absolutamente indispensable. Tres décadas después, la fortaleza ha sido reemplazada por algo similar a una plataforma en la nube a hiperescala. Esta plataforma puede ejecutar simultáneamente tus modelos de IA y el panel de control de tu director ejecutivo. Bienvenido al almacenamiento de datos en 2025.

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Is the Data Warehouse Dead? Exploring New Paradigms

20 Saturday Dec 2025

Posted by Martyn Jones in Inform, educate and entertain.

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AI, Analytics, Artificial Intelligence, cloud, data mesh, Data Warehouse, data-lakehouse, llm, medallion-architecture, technology


Martyn Rhisiart Jones

Segovia 20th December 2025

The Data Warehouse Is Dead. Long Live the Data Warehouse.

In 1992, Bill Inmon coined the term “data warehouse” and laid out four sacred rules: subject-oriented, integrated, non-volatile, time-variant. It was a blueprint for a fortress of truth, expensive, on-premises, batch-processed, and utterly indispensable. Fast-forward three decades. The fortress has been replaced by something that resembles a hyperscale cloud platform. This platform can run your AI models and your CEO’s dashboard simultaneously. Welcome to data warehousing in 2025.

The transformation has been tectonic. What used to be racks of hardware humming in a basement are now fully managed, serverless platforms. They scale compute and storage independently. These platforms charge you only for what you actually use. They let you ingest petabytes of streaming data without breaking a sweat. Snowflake, Google BigQuery, Amazon Redshift, Microsoft Fabric. These are the new names in town. They are rewriting the rules. Meanwhile, they quietly preserve the old ones.

Data volumes have exploded. Half of the data is semi-structured JSON or Parquet. The other half is increasingly unstructured and screaming for vector embeddings. Business leaders no longer wait until tomorrow morning for yesterday’s numbers; they want insights before the coffee is cold. And cloud computing has turned fixed-cost infrastructure into a utility you can dial up or down like a thermostat.

The result is a modern data warehouse. It looks very different from Inmon’s original vision. However, it still obeys Inmon’s core commandments.

It is still subject-oriented. Data is organized around business domains such as customers, sales, and inventory. This organization uses star schemas, snowflake schemas, or semantic layers. These layers are built with tools like dbt and Looker.

It is still integrated. ETL/ELT pipelines, data catalogues, and governance frameworks ensure that disparate sources become a single source of truth. Examples include Snowflake Horizon and BigQuery Data Catalogue.

It is still time-variant. Timestamped tables and Type 2 slowly changing dimensions are examples. Features like Snowflake’s TIME TRAVEL or BigQuery’s SYSTEM_TIME let you ask “what did we know on March 15?”

And it is still non-volatile, at least mostly. ACID transactions now allow controlled updates and deletes. (Hello, GDPR.) However, the analytical core remains append-only or change-tracked. It preserves history as sacredly as ever. The adaptations are what make the difference. Real-time streaming (Kafka connectors, Snowflake Streams, BigQuery Streaming) replaces overnight batch jobs. In-database ML and vector search power AI applications without moving data. Self-service semantic layers let business users explore data without having to beg IT for a new report. Storage and compute are decoupled. You can store exabytes cheaply. You can spin up thousands of concurrent queries without rewriting the budget.

So, does the modern data warehouse still follow Inmon’s principles? Yes. Unequivocally. The spirit lives on, even if the body has been rebuilt from the ground up.

The catch is that this evolution has made the platform choice more consequential than ever. If you choose incorrectly, you’ll be locked into a single cloud provider. You may be saddled with unpredictable costs. You could also be stuck with a system that can’t keep up with tomorrow’s AI workloads. Pick the right one, and you get a platform that delivers enterprise-grade performance. It provides ironclad governance and predictable pricing. It remains accessible to everyone from data engineers to the marketing team.

In 2025/2026, the data warehouse isn’t dead. It’s just been reborn as something faster, cheaper, and far more potent than Inmon might have imagined. The old rules still hold. The new ones are just a lot more fun.

Many thanks for reading.

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