GOOD STRATEGY REBELLION

Tag: Data Warehouse

  • TECH HOUR: Warehousing Your Data: A No-Nonsense Guide to the Right DBMS in 2026

    TECH HOUR: Warehousing Your Data: A No-Nonsense Guide to the Right DBMS in 2026

    Martyn Rhisiart Jones, Madrid, Friday 27th March 2026

    Warehousing Your Data: A No-Nonsense Guide to the Right DBMS in 2026

    Listen, you glorious data martyrs. You noble sufferers who have spent far too many evenings coaxing historical rows into slowly changing dimension tables while the rest of the office has gone home to sensible lives. You know the drill. The star schema looks perfect on paper, the fact table is append-only and cooperative, but then someone asks for last year’s corrected customer segments. Suddenly, your backfill script is performing open-heart surgery on a live production warehouse.

    In 2026, vendors are still shouting about infinite scalability and “agentic AI” (whatever that means this week), but what actually matters is a system that lets you shove yesterday’s data into those dimension objects without it feeling like a hostage negotiation.

    I have been asked to examine the most appropriate database management systems for effective data warehousing, with particular attention to the pain of backfilling dimensional tables. The brief is clear: functionality, reliability, scalability, extensibility, maintainability, performance, cost, ease of use, and a total lack of corporate nonsense.

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  • Datos de Forma Deliberada: Mejores Prácticas para EDW

    Datos de Forma Deliberada: Mejores Prácticas para EDW

    Martyn Rhisiart Jones, Madrid martes 24 de marzo de 2.026

    En el mundo en constante evolución del almacenamiento de datos empresarial (enterprise data warehousing), uno de los desafíos más persistentes y críticos es cómo expandir de forma inteligente las áreas temáticas y los datos asociados dentro de la base de datos del almacén de datos central (core data warehouse), manteniendo la integridad arquitectónica, la calidad de los datos y la gobernanza, sin adentrarse en consideraciones de data marts.

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  • MASTER CLASS: Mastering Data Warehousing: Balancing Pre-Emption and Back-Filling

    MASTER CLASS: Mastering Data Warehousing: Balancing Pre-Emption and Back-Filling

    Martyn Rhisiart Jones, Madrid Tuesday 24th March 2026

    In the ever-evolving world of enterprise data warehousing, one of the most persistent and critical challenges is how to intelligently expand subject areas and the associated data within the core data warehouse database, while maintaining architectural integrity, data quality, and governance, without venturing into data mart considerations.

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  • STRAIGHT TALK: Grok, What Do You Make of Martyn Rhisiart Jones’ Take on Big Data?

    STRAIGHT TALK: Grok, What Do You Make of Martyn Rhisiart Jones’ Take on Big Data?

    Sir Afilonius Rex,

    London, 15th February 2026

    Me: What do you make of Martyn Jones of goodstrat.com’s views on big data, are they correct, close or not true?

    [Grok thought for a while]

    Grok: Martyn Jones (of goodstrat.com) is one of the sharpest, most entertaining data contrarians out there. He’s been calling “Big Data” mostly bullshit since around 2014–2015, long before it became fashionable to say so. His Core Views (in his own blunt style): The whole 2010s Big Data circus (Hadoop, the 3Vs turned into 12 Vs including Vagueness, Vendibility, Vulgarity, etc.) was 80–90% vendor hype, consultant grift, and marketing snake oil.
    Most companies didn’t (and still don’t) have Google/Facebook-scale problems. Chasing petabytes of unstructured social media noise often distracted companies. They were diverted from cleaning and using the high-value operational data they already had.

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  • Building the Data Logistics Hub: Pieces and Parts – 2026/02/15 – Part 3

    Building the Data Logistics Hub: Pieces and Parts – 2026/02/15 – Part 3

    OLYMPUS DIGITAL CAMERA

    This is the brave new world of data!

    Martyn Rhisiart Jones

    Bandoxa, 15th February 2026

    Building the Data Logistics Hub: Pieces and Parts – 2026/02/15 – Part 3

    Guide

    This episode provides a comprehensive framework for the third installment in the series on the Data Logistics Hub (DLH). Martyn Jones conceptualised it as a technology-agnostic, centralised platform. Its purpose is efficiently moving, governing, and distributing data across organisations. This part expands on Part 1 (Challenges and Opportunities) and Part 2 (The Strategy). It focuses on the tangible “pieces and parts” of the DLH architecture. It outlines mandatory and optional elements. The episode also explores potential technologies. It examines key processes such as data pulling or pushing, translation from source to target, mapping, and data catalogues.

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  • Building the Data Logistics Hub: The Strategy – 2026/02/14 – Part 2

    Building the Data Logistics Hub: The Strategy – 2026/02/14 – Part 2

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    This is the brave new world of data!

    Martyn Rhisiart Jones

    Bandoxa, 14th February 2026


    Building the Data Logistics Hub: The Strategy – 2026/02/14 – Part 2Before I begin, remember this: “All data roads lead to the Data Logistics Hub.” They also lead from it. It is the Rome of the age of data, information, knowledge, and wisdom. Be prepared!

    Okay, we will now examine the Data Logistics Hub in terms of strategy, execution plans, and roadmaps.

    A high-level blueprint for a successful Data Logistics Hub outlines several requirements. These include principles, guiding objectives, an imagined “better world” and organisational alignment. Key trade-offs must also be considered, such as centralised versus federated and batch versus streaming, among others.

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  • Solucionando el Dilema del Almacén de Datos

    Solucionando el Dilema del Almacén de Datos

    Martyn Rhisiart Jones

    Madrid, sábado 7 de febrero de 2026

    Libros de Martyn Rhisiart Jones: https://www.amazon.es/stores/author/B086K5B97Y

    ¿Qué decir?

    Estaba leyendo un artículo escrito por Jeff Wilts y recomendado por Bill Inmon. Llegué a esta afirmación: «Teradata es un almacén de datos empresarial con todas las funciones». Para mí, la cosa fue aún más cuesta abajo a partir de ahí.

    Pero esto fue el golpe de gracia: «Databricks es una plataforma de datos unificada que puede comportarse como un almacén de datos».

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  • Building the Data Logistics Hub: The Challenges and Opportunities – 2026/02/13 – Part 1

    Building the Data Logistics Hub: The Challenges and Opportunities – 2026/02/13 – Part 1

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    This is the brave new world of data!

    Martyn Rhisiart Jones

    Bandoxa, 13th February 2026

    In this episode, we begin by honestly examining the pain points that make data logistics so difficult today. The challenges are siloed data and systems. There are also many data interchange point solutions. Quality is inconsistent, and there are security and compliance barriers. Additionally, data volumes are exploding. We then explore the transformative opportunities. These include faster time-to-insight and seamless collaboration across teams and organisations. The opportunities also feature monetisable data products and AI-ready flows.

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  • DATA WORLD: Data Warehouse Action: Big Business Drivers

    DATA WORLD: Data Warehouse Action: Big Business Drivers

    Masterclass on the side!

    Martyn Rhisiart Jones

    Madrid, Saturday 31st January 2026

    Martyn: The Enterprise Data Warehouse should be driven by business demand and nothing else.

    Ed: What does that mean in practice?

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  • DATA WORLD: Friends! Romans! Countrymen! Big Data is not Data Warehousing – 2026/01/29

    DATA WORLD: Friends! Romans! Countrymen! Big Data is not Data Warehousing – 2026/01/29

    Martyn Rhisiart Jones

    29th January 2026

    Hold this thought: To paraphrase the great Bob Hoffman, just when you think that if the Big Data babblers were to generate one more ounce of bull**** the entire f****** solar system would explode, what do they do? Exceed expectations.

    I am a mild mannered person. However, one thing that irks me is hearing variations on certain themes. These themes include phrases like “Data Warehousing is Big Data.” Another is “Big data is in many ways an evolution of data warehousing.” Lastly, some say “with Big Data you no longer need a Data Warehouse.”

    Big Data is not Data Warehousing. It is not the evolution of Data Warehousing. It is also not a sensible and coherent alternative to Data Warehousing. No matter what certain vendors will put in their marketing brochures or stick up their noses.

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