The frontier of AI has shifted from training to inference, from learning to acting. Yet our data infrastructure remains anchored in a batch-processing past, creating a fundamental mismatch between what AI systems need and what current architectures deliver. The Context Lake represents a new class of infrastructure purpose-built for this reality: a unified system where ingestion, transformation, and retrieval happen continuously on live data, enabling AI to act with perfect context at the moment of decision.
A new user signs up for your platform. Ninety seconds later, they add a credit card, connect a wallet, and try to withdraw a large sum. In the background, dozens of risk signals appear: the IP is from a high-risk subnet, the card was just used on another flagged account, and the device matches a known fraud ring. You have milliseconds to decide — but the risk signals in your data warehouse were last updated 5 minutes ago. In fraud, 5 minutes is an eternity. The fraudster has already moved to the next account.
This isn’t a bug. It’s the architecture — built for a slower era. We’ve spent years building large-scale, low-latency systems — from co-authoring a peer-reviewed VLDB paper to contributing FlinkSQL — and have seen where even the most advanced architectures fall short of delivering always-fresh, decision-ready context.
For the past decade, enterprises have invested billions in data lakes, warehouses, and lakehouses — infrastructure optimized for one primary goal: training better models on historical data. This made sense when the frontier of AI was model improvement, when competitive advantage came from better algorithms trained on larger datasets.
But the frontier has shifted. The challenge is no longer building models — it 's connecting them to reality. As models move from research to production, from batch predictions to real-time decisions, the bottleneck isn 't compute or algorithms. It 's context.
As Chamath Palihapitiya put it, “The real infrastructure bottleneck isn’t GPUs — it’s everything that happens after the model is trained.”
Today 's models — whether classical ML, large language models, or autonomous agents — are remarkably capable. What limits their value isn 't intelligence but information: the ability to access complete, current context at the moment of decision. We 've solved the learning problem. We haven 't solved the acting problem.
Once models enter production, they don 't just need more data — they need the right data at the right moment. This is context: data in motion, shaped and retrieved for the specific decision at hand.
Context isn 't a new type of data — it 's data transformed by urgency and relevance.
Consider how context manifests across AI systems:
The distinction is crucial:
A product catalog is data. The item a user just viewed, combined with their purchase history, current session behavior, live inventory, and real-time price optimization — that assemblage becomes context when retrieved in the 13 milliseconds before the page renders.
"The real infrastructure bottleneck isn’t GPUs — it’s everything that happens after the model is trained.”
Today 's dominant architectures — data lakes, warehouses, and their hybrid offspring, lakehouses — share a common ancestry and a common limitation. They were designed when the primary goal was understanding what happened, not deciding what should happen next.
Each generation brought improvements:
But they all share two fundamental constraints that make them unsuitable for powering real-time AI:
In short: lakes, warehouses, and lakehouses excel at explaining the past, but they can’t keep up with the present. And in AI, the present is where decisions — and value — are made. Meeting that demand requires more than faster batch jobs. It requires a completely different foundation.
When lakes and warehouses prove too slow, the natural instinct is to reach for databases — the transactional systems that already power production applications with millisecond response times.
Modern cloud databases have added capabilities like JSON support and vector search. These are useful advances — but they don’t change the core reality: databases are designed to record facts, not to assemble rich, decision-ready context. They excel at answering “What is?” but struggle with “What should we do?”
Without a shared, real-time context layer, a fraud detector, recommendation engine, and inventory API can each be “right” in isolation yet act on conflicting information. In AI, that fragmentation turns speed into misalignment — and misalignment into missed opportunity.
Some systems claim to bridge operational and analytical workloads, positioning themselves as "HTAP "(Hybrid Transactional/Analytical Processing) solutions. But in practice, they merely bolt small-scale analytics onto transactional engines, or add simple operational features to analytical systems. They 're fine for dashboards that summarize recent orders. They fail catastrophically at the demands of real-time AI.
True Context Lakes require capabilities that no existing "hybrid "system delivers:
Existing "hybrid "systems make trade-offs that seem reasonable in isolation but prove fatal for Context Lakes. They choose either consistency or scale, either freshness or throughput, either flexibility or performance. The Context Lake refuses these false choices. It requires the analytical power of a warehouse, the responsiveness of a transaction processor, and the flexibility of a multi-modal database — unified in a single engine. This isn 't an incremental improvement on HTAP. It 's a different category entirely.
Solving the context problem requires more than faster databases or smarter pipelines. It demands a fundamental rethinking of how data becomes context — a new architectural paradigm purpose-built for the age of AI inference.
Enter the Context Lake .
A Context Lake isn 't simply another data store or processing engine. It 's a unified infrastructure layer designed from first principles to deliver live, multi-modal context at the speed and scale of AI decision-making.
Where traditional systems optimize for either transactions or analytics, for either freshness or scale, the Context Lake refuses these false trade-offs. It provides both, simultaneously, in a single coherent system.
Under the hood, a production-grade Context Lake brings together capabilities no single system has delivered before:
This isn 't a collection of components stitched together — it 's a single system with unified semantics, consistent freshness guarantees, and seamless coordination across all operations.
The power of a Context Lake emerges from its ability to run a continuous, real-time loop that traditional architectures can only approximate in batch. This loop — which we call ITERATE — represents the fundamental operating model of context-driven systems:
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Traditional architectures can run a version of this loop, but each step requires different systems, each transition involves delays, and by the time the loop completes, the world has changed. In a Context Lake, the loop is native, continuous, and instantaneous.
When all operations happen in one system on shared state, something remarkable occurs —capabilities emerge that no collection of specialized systems can achieve.
Consider feature engineering. In traditional architectures, features are computed in batch, stored in feature stores, and served from caches. There 's always lag, always staleness, always drift between training and serving.
In a Context Lake, features are computed continuously as data arrives, stored in the same system that serves them, and retrieved with perfect point-in-time consistency. The same logic that computes features for training computes them for serving. There 's no drift because there 's no separation.
Or consider adaptive pricing. Traditional systems might update prices hourly based on batch analytics. A Context Lake can adjust prices continuously based on real-time demand signals, competitor actions, and inventory levels — all while maintaining consistency across channels and honoring business constraints.
These aren 't incremental improvements — they 're new capabilities that emerge only when the entire loop runs on unified, live state.
The value of the Context Lake becomes concrete when we examine real-world scenarios where milliseconds determine outcomes.
An ad platform has less than 50 milliseconds to decide which creative to show a user who just clicked through from a trending social post. The decision blends the user’s browsing history, live campaign budgets, current competitive bids, and the performance of similar creatives in the last few minutes.
In a traditional setup, some of this context lives in a database, some in a feature store, and some in logs processed hours later. The delay means bidding strategies can be outdated before they even run. In a Context Lake, all of it — structured campaign data, semi-structured clickstreams, vector embeddings of creatives — is ingested, joined, and scored in a single loop. The winning bid is chosen and served while the user is still on the page.
A user hovers between two products on your site. Moments later, they open your mobile app and see one of those products featured with a tailored message: “Only a few left in your size. Order now for free next-day delivery.”
This isn’t batch recommendations generated hours ago — it’s context in motion. The user’s real-time clickstream is combined with their past behavior and preferences, product embeddings, current stock levels, and urgency cues. All of this context is evaluated and acted on instantly, so the decision reaches the user while they’re still deciding.
Whether it’s stopping a fraudulent withdrawal before the funds leave, optimizing an ad bid before the auction closes, or personalizing an offer before the customer moves on — the real-time ITERATE loop turns raw events into live context, and live context into action. That’s something no batch-based system can match.
Building a Context Lake isn 't just an optimization — it 's an architectural imperative driven by fundamental shifts in how AI systems create value.
Large language models can process thousands of tokens per second. Recommendation engines make millions of predictions per minute. Autonomous agents take thousands of actions per hour. The velocity of AI inference is accelerating exponentially.
Yet most organizations serve this inference from infrastructure designed for human-speed analytics. It 's like powering a Formula 1 race with a fuel system designed for Sunday drives. The mismatch isn 't just inefficient — it 's physically impossible to bridge with incremental improvements.
The cost of model inference has plummeted — what once required specialized hardware now runs on commodity CPUs. The cost of poor decisions, however, has skyrocketed. A missed fraud detection costs thousands. A poor recommendation loses a customer. A delayed trade misses the opportunity entirely.
The ROI of infrastructure investment has inverted. Previously, we optimized for training larger models. Now, the return comes from better context. A modest model with perfect context outperforms a perfect model with modest context — and the gap widens every day.
Organizations that solve the context problem will operate at a different clock speed than their competitors. While others wait for batch windows, they 'll adapt continuously. While others approximate with stale data, they 'll decide with perfect information. While others coordinate across systems, they 'll act coherently from unified state.
This isn 't incremental advantage — it 's categorical superiority. It 's the difference between companies that leverage AI and companies that are limited by their infrastructure 's ability to support it.
The Context Lake represents more than new technology — it represents a new discipline, a new way of thinking about data in motion, context in time, and decisions in production.
Just as data engineering emerged to tame the complexity of big data, context engineering is emerging to master the complexity of real-time AI. Context engineers don 't just move data — they shape it for decisions. They don 't just build pipelines — they design continuous transformations. They don 't just optimize queries — they orchestrate context delivery at inference speed.
This discipline requires new tools, new patterns, and new mental models. It requires thinking in streams rather than batches, in milliseconds rather than minutes, in context rather than just data.
We 're at the very beginning of this transformation. The patterns are still being discovered. The best practices haven 't been written. The tools are still being built.
Early adopters won 't just implement technology — they 'll define the standards, establish the patterns, and shape the practices that the industry will follow for the next decade. They 'll build competitive moats not just from better models but from better infrastructure to serve them.
If you 've ever looked at a decision your system made and thought, "If only it had known..."— you understand the problem we 're solving.
If you 've watched value leak through the gaps between batch windows, if you 've seen models fail not from lack of intelligence but lack of information, if you 've felt the friction of coordinating across fragmented systems — you know why this matters.
The Context Lake isn 't just another database or data warehouse. It 's the foundation for a new generation of AI systems that can act as fast as they can think, that can maintain context as fluid as the world they operate in, that can turn intelligence into action without compromise.
Every generation of infrastructure has enabled a new class of applications. Mainframes enabled global computation. Databases enabled online transactions. The cloud enabled infinite scale. Data lakes enabled machine learning.
The Context Lake enables something new: AI systems that operate at the speed of reality.
We no longer just analyze data at rest — we act on context in motion. We don 't just learn from history — we respond to the present. We don 't just predict the future — we shape it, one decision at a time, with perfect context at the moment of action.
The organizations that recognize this shift, that invest in context infrastructure with the same commitment they once invested in data infrastructure, will define the next era of technological competition.
The future isn 't about who has the most data or the best models. It 's about who can transform data into context, context into decisions, and decisions into outcomes — all at the speed of now.
That future starts with the Context Lake.
