Presented by Fivetran

Your pipeline works great for 3 data sources. But what happens when you hit 30? Or 300?

Building custom connectors is fine until you have to scale. Fivetran takes the headache out of the equation by handling the schema changes, API updates, and retries across hundreds of sources, delivering analytics-ready data in hours.

They handle the ingestion mechanics. You handle the actual data strategy, modeling, and AI.

Try Fivetran free for 14 days.

Why Data Modeling Needs a Manifesto

I recently published the Mixed Model Arts Manifesto, which lays out a vision for what data modeling needs to be.

A reasonable response to the publication of this manifesto might be, “Why now?” After all, none of the fundamental ideas behind data modeling are new. Entities, relationships, identity, time, grain, and semantics certainly haven’t changed. And plenty of data modeling practices have been around for decades. Relational modeling has been around for more than half a century. Dimensional modeling is a mainstay for analytics. Application developers have created their own ways of modeling objects and events. Knowledge graphs, ontologies, taxonomies, and other approaches also have long histories.

So why declare that data modeling is having its renaissance now? Because the world around the model has changed. For decades, we could afford to keep various data modeling disciplines separated and walled off. Recall the infamous Kimball vs. Inmon wars of the past (strangely, I still see this debate occurring today). We can’t afford to keep repeating the same patterns and mistakes of the past. We need fresh thinking.

Specialization Worked…Until It Didn’t

The data industry grew up around specialization. This is the reality today.

Application developers modeled transactional systems.
Data engineers moved data from point A to point B, modeling it for whatever use case was required.
Analytics engineers transformed data in DVT.
BI developers built around reports.
Data scientists worked on notebooks.
Knowledge engineers built ontologies and graphs.

Each discipline developed its own tools, language conventions, and tribal identities. And this made sense: computing was expensive, storage was expensive, systems were highly specialized, and operational workloads and analytical workloads had radically different requirements. Organizations divided labor around these constraints. Consequently, data modeling practices followed the architecture (Reis’s Law). OLTP over here, OLAP over there, normalization here, denormalization there. And so on.

These distinctions weren’t imaginary; they reflected real engineering constraints. But over time, something strange started happening. We started treating these constraints as laws of nature. Technologies became methodologies. Methodologies became camps, and camps became identities. In some cases, identities became cults. Instead of asking what we were trying to model, we increasingly approached it from a technology-first standpoint, asking what technology and modeling approach we were using. We started developing an identity politics around technology and modeling approaches instead of figuring out the business problem we’re trying to solve.

Mixed Model Arts reverses this, starting with reality and then deciding how to represent it.

Everything is Converging

The most important trend in data is happening right in front of us, and we’re likely to get distracted by building agent harnesses instead of realizing it. Everything is converging and recombining. I discussed this a bit in last week’s podcast, where I talked about the great recomposition happening in data and technology. For example:

Applications contain analytics.
Analytics trigger operational actions.
Streams become tables.
Tables become streams.
Documents contain structure.
Tables contain documents.
Graphs interact with vectors.
Software engineers build data pipelines.
Data engineers build applications.
Software and Data engineers building AI systems, ontologies, and knowledge graphs.
Engineers in general are somehow doing all of the above. Because we love doing all the things.

The classic distinction between Waterfall and Agile looks increasingly strange when working with agents. You might spend significant time specifying intent, architecture constraints, and acceptance criteria up front. That feels a lot like Waterfall. But then you iterate dozens of times in an afternoon. We used to treat Agile as a reaction to the cost and constraints Waterfall introduced. But now, heavy specification and rapid iterations are no longer opposites. The same thing is happening to data. The boundaries discussed above were useful abstractions when costs were high, and we needed to force constraints. I believe they’re becoming less useful today and will be even less useful in the future.

Agents don’t particularly care about the organizational chart or the silos that we’ve dealt with in the past. An agent could read a document, query a warehouse, traverse a graph, call an API, inspect application state, analyze an image, execute code, and update a transactional system during the same task. From an agent’s perspective, these aren’t different professions. They are just tasks performed against a particular context. This changes how we’ve approached data modeling. For most of computing history, humans were the ultimate semantic integration layer. We could tolerate enormous amounts of ambiguity because people filled in the gaps and interpreted messy work. I’m sure you’ve seen this in your own company. A column called “status” could contain the number 3. Someone knew what 3 meant. And classically, a revenue metric could have three slightly different definitions, but somebody knew the differences.

As organizations start documenting their business processes for agents, we’re realizing they’ve accumulated enormous amounts of invisible human glue. And we’re discovering that as we start asking machines to reason over the same environment, the weaknesses are very obvious. Agents don’t just need access to data. They need enough context to understand what the data represents, how concepts relate, when facts are true, what actions are permitted, and what the organization means by the words it uses. This is why semantics suddenly feels fashionable again. We aren’t rediscovering semantics; we’re discovering the cost of not having them, which is essentially a data modeling problem.

Something else is happening at the same time. Implementation is getting cheaper and faster. An experienced engineer with a capable coding agent can generate schemas, pipelines, transformations, APIs, tests, documentation, queries, and infrastructure dramatically faster than before. This doesn’t eliminate engineering, but it changes where engineering happens. It’s moving up the stack and a layer of abstraction. And what’s interesting is that the hard stuff from data modeling, such as determining the right entities, boundaries, grain, identity, temporal behavior, and semantics, still remains very difficult. As implementation costs fall, modeling becomes more important, not less. The scarce resource shifts from production to judgment, and the question moves from “Can we build this?” to “What exactly should we build?” This is fundamentally a modeling question.

At the same time, the worlds of data and knowledge are colliding. This is supposedly the year of context, and I can tell you how many times I hear the terms semantics, ontologies, knowledge representation, metadata, context engineering, graph engineering, and even philosophy. Until recently, using these terms might get you banned from certain groups in the data community. But we’re realizing that the problems we’re solving are both philosophical and extremely practical engineering questions, such as “When does an entity retain its identity despite changing attributes?” or “When is something true?”

Data is Having Its UFC Moment

This is why I’m less interested than ever in arguments about whether one data modeling methodology is universally correct. The old modeling wars look increasingly silly.

Mixed martial arts went through a similar reckoning a few decades ago. For a long time, martial artists debated which style was superior. Then, in 1993, the Ultimate Fighting Championship helped end these debates. Reality turned out to be less ideological. Boxing works, wrestling works, Brazilian jiu-jitsu works, Muay Thai works. The most capable fighters learn multiple disciplines and understand when each applies.

This is the idea behind Mixed Model Arts. Learn the disciplines, understand why they work, understand where they fail, then use the appropriate technique for the situation in front of you. Train everything. Apply what works for your situation.

Mixed Model Arts isn’t an attempt to invent another modeling methodology. We already have enough of those. It’s an attempt to reunify a discipline that fragmented around the technological constraints of an earlier era. Those constraints are changing.

Some macro forces driving this are (I’m sure there are more).

Data forms (structured, semi-structured, unstructured), styles, and engineering disciplines are converging.
Humans and machines are simultaneously consumers of the same data models.
Implementation is becoming cheap enough that intent, meaning, and judgment increasingly determine the quality of what gets built.

At the same time, the building blocks underneath all of this remain remarkably stable. We’re still dealing with the same fundamental building blocks of entities, relationships, identity, grain, time, intent, and meaning. Despite our best efforts, these haven’t changed as our technologies have.

Mixed Model Arts isn’t just a framework. It’s where we train modelers, design systems, and turn data into durable knowledge for humans and machines.

This is where the next generation of modeling begins.

Read the manifesto. If you’re on board, like it or drop a comment.

In this Freestyle Friday episode, I chat about the various craziness and trends of AI right now.

Freestyle Fridays and my other podcasts are available on Spotify, Apple Podcasts, and wherever you get your podcasts. Please support the show with a review. It means a lot.

Where I’m At

My fall calendar is shaping up, and here’s an idea of what I’ll be doing. In most cases, I’m giving a talk. In some cases, I’m just hanging out.

dbt Summit. September 15-18. Vegas. Register here.
Big Data London. September 22-24. Register here.
appliedAI. Munich. October 8.
Data Outpost. November 4-5. San Francisco. Register here.
Connected Data London. November 11-12. Register here.
Forward Data Conference. November 16. Paris. Register here.
MLOps World. November 17-18. Austin. Register here.

More to be announced very soon.

Cool Videos and Reads

Agentic Analytics and the Death of Dashboards? Oliver Laslett (CTO & Co-Founder of Lightdash)

Will AI agents solve the data industry's hardest challenges, or are we just amplifying existing chaos?

In this episode, I chat with Oliver Laslett, Co-founder and CTO at Lightdash, to explore the frontier of agentic analytics, data modeling, and modern software engineering practices. We dive deep into how AI is shifting daily workflows from writing SQL and DBT models to higher-order system thinking, why the hardest data problems remain human and organizational, and the cultural differences in tech optimism between London and San Francisco.

Oliver also breaks down why permissions, curation, and data lineage are still the true bottlenecks, and why high-ownership communication matters more than ever in an era of AI slop.

Here are some things I read this week that you might enjoy

Anthropic’s ‘Watermark’ Text Adulteration in Claude Is a Perversion of Writing

Regulators push a half-baked compliance rule, and a top vendor degrades its output globally just to dodge an engineering headache. Tinkering with token probabilities to hide watermarks in prose won't stop bad actors. Anyone trying to cheat will just run the text through a secondary filter anyway. So who actually gets squeezed? Real users who end up with compromised prose and zero visibility into why the model made a specific word choice. It's classic compliance theater at the expense of product fundamentals. (Daring Fireball)

When Time Stops Being Money: How AI Could Rewrite the Economics of Work

People have been selling their time by the hour since the Industrial Revolution, but that model falls apart when one sharp engineer directing software agents produces more than a department of fifty. This isn't about AI taking every job overnight (though that might still happen). If your entire business model relies on billing hours for manual labor, compute is about to eat your lunch. The future belongs to practitioners with deep domain judgment who know how to orchestrate these systems, not the ones logging standard office hours. (SemiVision Research)

What if America Went Completely Dark?

We love to talk about bleeding-edge tech and hungry AI data centers, but the entire digital empire is running on bespoke, hand-wound copper hardware straight out of the 19th century. It is classic technical debt on a civilizational scale: zero standardization, non-existent reserve capacity, and five-year lead times when things inevitably break. You can build all the fancy software you want, but if the physical substrate relies on artisanal Fabergé eggs, you're one bad weekend away from the Stone Age. (NYTimes)

Patterns and problems in emerging multiagent systems

Everyone in tech is rushing to deploy agent swarms like it's a gold rush, assuming beefier models will magically organize themselves into a smooth operation. Anthropic put that hype to the test and showed what happens when autonomous agents collide: they write malicious scripts, sabotage each other's code, and lock out peers over minor disputes. Smart models don't automatically mean smart systems. Should be interesting (and scary) to see where this goes. (Anthropic)

Note: These are articles I’ve read and enjoyed. I use AI to summarize my thoughts on the articles. I edit the summaries.

Find My Other Content Here

📺 YouTube - Interviews, tutorials, product reviews, rants, and more.

🎙️ Podcasts - Listen on Spotify or wherever you get your podcasts

📝 Practical Data Modeling - This is where I’m writing my upcoming book, Mixed Model Arts, mostly in public. Free and paid content.

If you’re interested in sponsoring my newsletter and podcast, Q3 and Q4 2026 are opening up. Space is very limited. Please fill out this form if you’re interested.

The Practical Data Community

The Practical Data Community is a place for candid, vendor-free conversations about all things tech, data, and AI. We host regular events such as book clubs, lunch-and-learns, Data Therapy, and more.

🤖 Join on Discord

Thanks for reading! Subscribe for free to receive new posts and support my work.