👉🏼 How Large Behaviour Models Are Rewriting Data Engineering Pipelines

Dear Reader…

🤔 DATA & AI GOVERNANCE MEETUP ALERT:

📆 IBM Sydney, Thursday 11 Sept @ 12noon 

In 2025, generative AI is no longer just a digital desk worker—churning out emails, summarising reports, or drafting code. It’s stepping into the physical world. Machines are not only reasoning but acting, powered by a new class of models known as Large Behaviour Models (LBMs). For data engineers, this isn’t just another incremental shift in model architecture. It’s a re‑wiring of the very workflows, tools, and mindsets that underpin the data management practices.

If the early days of AI were about language and text, the LBM era is all about behaviour and embodiment. Robots that adapt on the fly. Systems that don’t just process words, but watch and learn from human actions. And critically—pipelines that no longer follow the neat, batch‑based Extract–Transform–Load rituals we know so well.

This week we’re digging into how LBMs are upending data engineering. What do workflows look like when data is no longer static but streaming from the messy, unpredictable physical world? What new skills will tomorrow’s engineers need? And what risks—ethical, social, and technical—sit beneath the glossy promise of humanoid robots folding laundry or performing complex industrial tasks?

Part I: The Rise of Embodied Intelligence

Let’s rewind briefly.

The past five years saw Large Language Models (LLMs)—OpenAI’s GPT‑4, Google’s Gemini, Anthropic’s Claude—trained on terabytes of internet text, achieving versatility across countless textual tasks. A smaller but emerging movement of Small Language Models (SLMs) grew alongside—nimbler, domain‑tuned, cheaper to run.

But both camps shared the same fundamental weakness: they lived in a world of words.

An LLM can tell you, step by step, how to tie a sailor’s knot. But ask it to physically tie one, and you’re stuck explaining yourself to a laptop screen. This is where LBMs step in.

Large Behaviour Models don’t just read text or parse tokens. They learn from action—watching demonstrations, analysing sensor streams, and simulating new behaviours. They fuse language with proprioception, vision, and haptics. And crucially, they work in a feedback loop with the world itself.

Much like a toddler riding a bike, LBMs learn by trial, error, wobbling, falling, and trying again. In human terms, we’d call it “experience.” In machine terms, it’s real‑time data flywheels—constant cycles of collect → act → sense → refine.

This shift isn’t just academic. It crashes headlong into one of the core professions that make AI real: data engineering.

Part II: The Death of Batch

For decades, data engineers have lived with batch-oriented pipelines. Collect a static corpus. Clean it up. Transform it into tidy tables. Tokenise it for training. Deploy it once the data jobs finish overnight.

This model served LLMs and SLMs well, where the training diet was frozen snapshots of human text: last year’s Wikipedia dump, terabytes of scraped forums, digitised legal decisions.

But LBMs break this paradigm completely.

Welcome to the Data Flywheel

Instead of neatly bounded datasets, LBMs live off continuous streams:

• Robotic sensors producing thousands of measurements per second—force, torque, acceleration, proprioception.

• Camera feeds and lidar scans, pumping out frames far slower but demanding precise spatial alignment.

• Human teleoperation inputs, where VR‑driven operators guide robot bodies, each subtle muscle twitch captured as multimodal training data.

• Simulation data—synthetic but designed to cover rare “edge cases” that rarely appear in the physical world.

Together, these create a multi-rate, multi‑modal, asynchronous mess that data engineers must transform into usable signals. Alignment isn’t optional; it’s survival. Imagine trying to teach Atlas (Boston Dynamics’ generalist humanoid) how to carry a tyre, with the haptic data arriving milliseconds out of step with the camera feed: chaos ensues.

Suddenly, the familiar idea of ETL/ELT looks almost quaint. Instead, engineers are building what researchers dub a “data circulatory system”—living, real‑time architectures, always pumping, never still.

Part III: The New Engineering Workflow

So how does a typical LBM‑era workflow look in practice? Let’s walk through it chronologically.

1. Ingestion: Beyond APIs and Logs

Forget CSV dumps. Data ingress for LBMs looks like:

• Teleoperation rigs streaming VR‑captured hand and body kinematics.

• Onboard robot hardware blasting multi‑kilohertz accelerometer readings.

• Edge AI chips processing raw sensory input before beaming only critical slices to cloud nodes.

This demands distributed, low‑latency event‑driven architectures. Kafka‑like systems aren’t just handy extras—they’re the backbone.

2. Transformation: Time is Everything

Merging data from different sensor modalities isn’t trivial. Aligning a 30fps video with a 1000Hz motion signal requires:

• Real-time temporal alignment, correcting for latency drift.

• Spatial transformations, mapping signals back into consistent coordinate frameworks.

• Semantic stitching, ensuring an operator’s arm movement aligns to what the robot "saw".

Traditional row‑and‑column tables falter here. Engineers are turning to vector databases and graph‑based data storesto unify structured and unstructured elements.

3. Curation: Beyond Cleaning, Towards Learnability

For LLMs, cleaning meant removing spammy web text or normalising token frequencies. For LBMs, it’s existentially harder:

• Was the human operator’s demonstration smooth and efficient—or a sloppy move that, if learned, could doom the robot?

• Does this movement carry enough “mutual information” between action and state to be worth the compute budget?

• Does the dataset balance scenarios across edge cases, or reinforce subtle biases about which objects or people are “normal”?

This redefines the data engineer’s job from janitor to quality assessor of behaviour itself. Teams already explore programmatic scoring techniques to classify demonstration data by “learnability” or efficiency. That’s far more subjective—and dangerous—than scrubbing swear words from a corpus.

4. Training & Deployment: Cloud Meets Edge

The LBM era is pushing a hybrid compute model.

• Gigantic base model training (hundreds of millions or even billions of parameters) still belongs to cloud clusters—NVIDIA’s DGX boxes, hyperscaler infrastructure.

• But inference—the actual decision about “should the robot turn left or right?”—must occur on‑device, often within milliseconds.

Enter the era of edge AI accelerators like NVIDIA’s Jetson Thor. These chips sit inside robots themselves, hosting sophisticated behavioural policies without constant cloud roundtrips.

For data engineers, this creates yet another workflow twist: designing synced data pipelines that straddle edge and cloud. Logs, retraining data, corrections—all must flow back into the global flywheel without choking bandwidth or spiking latency.

Part IV: Case Studies at the Edge of Reality

The Atlas humanoid project from Boston Dynamics and the Toyota Research Institute provides the most vivid window into how this works.

Here, a 450M‑parameter diffusion transformer powers whole‑body control. Instead of piecemeal modules (one system for walking, another for lifting), Atlas runs off a single, behaviourally unified LBM.

The training relies on high fidelity teleoperated demonstrations in VR, combined with simulation‑heavy reinforcement and trial‑error learning. Across months, Atlas gains skills ranging from flipping furniture to tugging heavy tyres—tasks that would have been nightmare‑ish to hand‑engineer with brittle conventional code.

For engineers on the back‑end, this means building pipelines not just to capture “did the task succeed?” but every subtle temporal contour of each movement. The richness of that data is the real driver of LBM quality.

Part V: The Risks You Can’t Batch Away

This industrial revolution doesn’t arrive without dangers. In fact, LBMs raise ethical and technical stakes far higher than their linguistic forebears.

1. Bias Can Now Break Bones

When an LLM outputs a biased sentence, the worst outcome is reputational harm or misleading content. When an LBM incorporates data biases into behaviour, the stakes edge into physical harm.

A household robot that subtly misinterprets gestures from people with certain physical characteristics is not just unhelpful—it’s dangerous. If a robot trained disproportionately on athletic young demonstrators learns brittle responses, how does it behave when interacting with elderly or physically impaired users?

2. Privacy Becomes Bodily

LBMs rely on watching human actions. But the same systems that record operators tying knots or pouring milk could just as easily monitor everyday behaviour at scale. What happens when physical behavioural surveillance turns into the next fine‑grained advertising dataset?

3. Economic Concentration

Training LBMs remains prohibitively capital-intensive. Like LLMs before them, the industry risks consolidating around a handful of hyperscalers. Start‑ups and SMEs may fall behind, creating a tiered AI economy in which only monoliths wield embodied AI at scale.

Part VI: Strategic Recommendations for Data Engineers

If you’re a data engineer, what does survival—better yet, leadership—in this new paradigm demand? Several priorities crystalise:

1. Re‑architect for streaming. Master event-driven, distributed data systems. Forget batch as the default mental model; the LBM world pulses continuously.

2. Build multimodal literacy. Get comfortable aligning vision, audio, proprioception, haptics. These aren’t “exotic extras”; they’re the bread and butter of embodied AI pipelines.

3. Curation as craft. Treat behavioural data as precious. Explore programmatic “information‑richness” scoring. Understand that one good demonstration can outweigh a thousand mediocre ones.

4. Edge‑to‑cloud fluency. Know the hardware stacks. Optimise not only for cloud throughput, but for Jetson‑scale inference and bandwidth‑constrained sync.

5. Embed ethics in design. Bias and safety aren’t academic afterthoughts—they can have physical consequences. Bake in bias monitoring, explainability, and human‑in‑the‑loop oversight at the pipeline level.

LBM’s: The Circulatory System of Next Generation AI

LBMs herald a second revolution in AI—shifting from language-only intelligence to embodied, physical capability. For data engineers, this is both daunting and exhilarating. The pipelines of yesterday—frozen, batch‑oriented, static—are being melted down and recast into real‑time circulatory systems.

You are no longer building plumbing for stale corpora of text. You’re becoming the cardiovascular architect of robots that see, feel, touch, and act. Pipelines aren’t passive anymore: they’re alive, flowing, rhythmic, tightly coupled with the unpredictable pulse of the physical world.

The challenge might be immense, but the opportunity is equally breathtaking. Because whoever builds these pipelines isn’t just engineering data—they’re building the infrastructure for the next leap of intelligence itself.

That’s a wrap for this week

Happy Engineering Data Pro’s