Tag Archives: cloud

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

14 Saturday Feb 2026

Posted by in data mesh, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Inform, educate and entertain., information, Information Supply Framework, Knowledge, knowledge management, Martyn Jones, Masterclass

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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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DATA WORLD: The Risks of Using Databricks for Data Warehousing

09 Monday Feb 2026

Posted by in Inform, educate and entertain.

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Get that Databricks Out of My Face!

Martyn Rhisiart Jones

Madrid, Monday 9th February 2026

You need not loathe Databricks outright. It is perfectly defensible if you do. This is particularly true when your principal objective is classical data warehousing. This includes structured BI reporting, dependable SQL analytics, and a governed single source of truth for business metrics. It also entails semantic clarity and predictable costs for read-heavy workloads.

There are solid, well-trodden reasons for that caveat. Many experienced data warehousing practitioners see Databricks as an awkward or even risky primary platform for traditional warehousing. This view is shared by some of the field’s foundational figures. The concern is not ideological. It is architectural. When used as a warehouse, Databricks often reproduces exactly the pathologies criticised in enterprise data programs. These include unnecessary complexity, misdirected effort, and the perennial executive question, “Which number should I trust?”

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DATA WORLD: The Truth About Data Lakehouses: Hype vs. Reality – 2026/01/18

16 Friday Jan 2026

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Hype and Misleading Claims Surrounding Data Lakehouses

Martyn Rhisiart Jones

Bandoxa, 17th January 2026

Read my absolutely fabulous books: http://www.goodstrat.com/books

Let’s cut through the noise.

You’ve probably heard the pitch: A data lakehouse is the elegant hybrid. It finally gives you the best of both worlds.

  • The low-cost, massively scalable, schema-on-read flexibility of a classic data lake (perfect for ingesting raw, unstructured, petabyte-scale chaos).
  • The structured querying speed, ACID transactions, governance, and reliable performance of a traditional data warehouse.
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The GigaOm Data Lake Report

12 Monday Jan 2026

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Nonsense on parade

Martyn Rhisiart Jones

Madrid, Tuesday 13th January 2026

This GigaOm report is a classic vendor-sponsored benchmark. Fivetran picked the competitors and shaped the test scope. GigaOm (as explicitly disclosed) executed it “as-is” with “compatible configurations subject to judgment.” It’s marketing material presented as independent research. It has several structural weaknesses. These weaknesses make the 77–95% cost savings claim highly misleading for most real organisations.

1. It only measures ingestion compute, not true TCO.

The report repeatedly calls itself a “TCO report,” but it explicitly excludes:

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Future Data Trends: Insights for 2026 Leadership

21 Sunday Dec 2025

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Martyn Rhisiart Jones

Segovia, 20th December 2025

Top 5 Trends Shaping the Future of Data, Analytics, and AI Leadership in 2026

I’ve had the privilege of leading a groundbreaking global study in partnership with a strategic client. During this study, I spoke with many key Chief Data Officers (CDOs). I also engaged with Chief Data & Analytics Officers (CDAOs) and AI executives across industries and geographies. The insights reveal how organisations are changing their data and AI strategies. They aim to drive real business value in an increasingly complex world.

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Is the Data Warehouse Dead? Exploring New Paradigms

20 Saturday Dec 2025

Posted by in Inform, educate and entertain., Polemic

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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.

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The Evolution of Data Warehousing: Principles for 2026

20 Saturday Dec 2025

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Martyn Rhisiart Jones

Segovia, 20th December 2025

Introduction

The data warehouse has undergone a profound transformation over the past decade. Expensive, rigid, on-premises systems were once built for batch reporting. These systems have now evolved into cloud-native, highly scalable platforms. They are designed to meet the demands of today’s data-driven organisations.

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The Truth About Data Lakehouses: Hype vs. Reality

20 Saturday Dec 2025

Posted by in Inform, educate and entertain., Polemic

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Hype and Misleading Claims Surrounding Data Lakehouses

Martyn Rhisiart Jones

Segovia, 20th Devember 2025

A data lakehouse is marketed as a hybrid architecture. It combines the low-cost, flexible storage of data lakes. These lakes handle raw, unstructured data at scale. It also offers the structured querying, performance, and ACID (Atomicity, Consistency, Isolation, Durability) transactional capabilities of data warehouses. Vendors like Databricks coined the term. Snowflake and others promote it as a platform for analytics. It serves AI and machine learning needs. This approach eliminates data silos and reduces redundant copies. It enables cost-effective scaling through open standards and decoupled storage and compute models. However, much of this is hype driven by vendor marketing. Vendors promote it as an evolution from “data swamps” (unmanaged data lakes) and rigid warehouses.

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Understanding Medallion Architecture: The Myth of Layers

20 Saturday Dec 2025

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Martyn Rhisiart Jones

Aranjuez, 20th December 2023

The Medallion Architecture, or: How We Learned to Stop Thinking and Love the Bronze Table

Somewhere, right now, as you read this, a data engineer is renaming a table. Again. They’re calling it Bronze, because that’s what the priests say it must be called. Bronze. Not “raw”, not “stuff we barely understand”, not “this arrived at 3AM and no one knows why”. Bronze. They do it solemnly, reverently, like a monk copying out the Book of Leviticus but with YAML. And when they are done, they will feel a sense of calm, because order has been restored to the universe. Temporarily.

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The Future of Data Warehousing: Boring Yet Effective

05 Friday Dec 2025

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Why Data Warehousing Is the Boring, Brutal, Beautiful Future of Data

Martyn Rhisiart Jones

A Coruña, 5/12/2025

The modern enterprise data stack looks less like technology and more like cyberpunk plumbing. Pipes are rerouted through pipes. Valves are attached to valves. Layers are placed on top of each other until the whole thing resembles a drawing. It’s like a creation made by a caffeinated child let loose on a whiteboard. Data lakes are here. A data mesh is over there. A lakehouse is balanced precariously on top. It resembles a loft extension built by someone who learnt carpentry from TikTok.

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STRAIGHT TALK: Bulls*** at the Data Lakehouse – Revisited

02 Sunday Nov 2025

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Martyn Rhisiart Jones

Spain, 2nd November 2025

And So It Begins

Five years ago, I wrote a piece called Bullshit at the Data Lakehouse, which caused quite a bit of a stir on social media, so I’m taking this opportunity to revisit it and revise it and to offer it as a historical reference with ramifications for today and the future.

My piece began in this spirit and then progressed rapidly.

Move over, big data hubris, data mesh revisionism, and data lake stupidity, there’s a newer, thicker and far bigger arsehole on the block. And it goes by the unbelievably idiotic name of data lakehouse. It is being hailed as a new paradigm, but in reality, it is a naive, dishonest, and disruptive fraud. So what’s occurring?

The gutter-snipes, hustlers and useless pundits who failed to make big data and data lakes the success of the 2010s have set their vulture-eyed sights on data warehousing. It’s not smart, it is not funny, and it does no one any service.

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Origins of Relational Databases: Transaction Processing Misconceptions

03 Thursday Oct 2024

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Were relational database management systems first used for transaction processing?

Martyn Richard Jones, Madrid, 3rd October 2023

Narrator: Relational database theory is over fifty years old. Relational database technologies go back more than four decades. Some claim that relational databases were first used for online transaction processing applications. Say what?

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Data Warehousing means monolithic databases?

27 Friday Sep 2024

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Data Warehousing means monolithic databases?

Martyn Jones, Brooklyn, 27th September 2024

Narrator: Another false meme doing the rounds is that Data Warehousing necessarily means monolithic databases. This is not what data warehouses have been for many businesses.

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The Awkward Squad – Big data informs

11 Saturday Oct 2014

Posted by in Big Data, BS, Data Warehouse, disinformation, information, Knowledge, wisdom

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Martyn Richard Jones

Remaster for 2026

I am a sceptic. Part of the awkward squad of troublemakers.

People who ask questions and who won’t stop asking.

People who won’t take bullshit for an answer.

People who are not preprogrammed to unquestioningly follow specific paths.

But to question everything.

Such as, “What the feck’s that all about then?” Continue reading →