The AI Era Is Changing Everything About Data
We are entering a new era of computing. AI is evolving from systems that answer questions into autonomous agents that reason, make decisions, and take action. And this transformation is fundamentally changing what we expect from data infrastructure.
For decades, organizations built data platforms around a relatively predictable model: collect information, process it, store it, and make it available for analysis. Data pipelines could run every few hours, sometimes even once a day. For many applications, that was good enough.
AI changes that equation. Intelligent applications need to continuously consume information from an expanding universe of sources, including applications, databases, event streams, logs, and connected systems. As AI agents become more autonomous, they will generate even more activity, creating a continuous cycle of data consumption, processing, and creation.
The result is a new set of requirements: massive scale, near-real-time freshness, continuous processing, and dramatically better cost efficiency.
More importantly, data must arrive not only quickly, but ready to use. An AI agent making a decision needs accurate, current, and meaningful information, not raw events buried inside an expensive pipeline.
We believe this transition requires a fundamental rethink of the technology that powers modern data platforms. And that's why we built Eon Yukon.
How our Journey Started
At Eon, data has always been at the center of everything we do. Today, Eon protects more than an exabyte of enterprise data across cloud environments. Operating at this scale has given us something incredibly valuable: a deep understanding of how data behaves, how cloud storage works, and where the real costs and limitations of large-scale infrastructure come from.
Protecting enormous volumes of data requires solving some of the hardest problems in distributed systems. We deal with billions of objects, constantly changing datasets, metadata management, data movement, deduplication, and storage optimization.
At this scale, every unnecessary operation matters. Every additional read, every redundant copy, and every inefficient use of compute resources can translate into significant infrastructure costs.
We've learned that the biggest challenge isn't simply storing or processing more data. It's doing so efficiently, reliably, and economically as the scale continues to grow.
We also learned that traditional boundaries between storage and processing create enormous opportunities for optimization.
When you understand how data is physically stored, accessed, organized, and moved across cloud infrastructure, you can start designing fundamentally better ways to process it. That understanding became the foundation of Yukon.
The Problem with Yesterday's Data Engines
Technologies like Apache Spark and Apache Flink helped define modern data processing. They enabled organizations to process massive datasets across distributed infrastructure and created the foundation for today's data ecosystem.
But they were designed around architectural assumptions and workload patterns that don't always align with the requirements emerging today.
Consider what happens when an organization needs to continuously ingest billions of events, transform them, maintain fresh analytical tables, and make those tables immediately available to applications and AI.
Processing the incoming data is only the beginning. Traditional architectures frequently require additional work to compact small files, reorganize data, maintain indexes and statistics, calculate aggregations, and keep tables optimized for queries. These operations often involve separate jobs, additional infrastructure, and repeated reads of data that has already been processed.
As volumes grow, these inefficiencies compound. Streaming architectures introduce challenges of their own: expensive checkpointing, uneven data distribution, hot partitions, and compute resources waiting on coordination rather than processing useful work.
The common response has been to add more compute, more pipelines, and more operational complexity. But what if the answer isn't adding more infrastructure? What if we could eliminate much of the unnecessary work in the first place? That became the fundamental question behind Yukon.
Introducing Eon Yukon: Built from First Principles
Rather than extending an existing processing framework, we decided to approach the problem from the ground up. We designed Yukon as a new, cloud-native data engine, built in Rust and architected specifically for the scale, performance, and economics of modern data workloads.
Our goal was ambitious: create one of the fastest and most cost-efficient ways to turn massive volumes of raw data into continuously optimized, ready-to-use information. To get there, we challenged several assumptions embedded in traditional data architectures.
- First, read data once and do everything possible while it's already in memory.
When Yukon ingests an event, it can decode, filter, transform, deduplicate, aggregate, and calculate statistics as part of the same processing flow.
Instead of repeatedly reading the same data through multiple independent jobs, Yukon combines these operations into a single efficient pass. The result is less data movement, fewer compute cycles, and significantly less infrastructure to manage.
- Second, eliminate architectural bottlenecks rather than scaling around them.
Yukon separates ingestion, writing, committing, and compaction into independently scalable services. Workers stay focused on processing data instead of waiting for global coordination. Work is distributed to handle uneven traffic and hot partitions, while an architecture without global checkpoints allows processing capacity to scale and recover without stopping the entire system.
Rust gives us precise control over memory and compute resources, but the real performance advantage comes from combining that efficiency with an architecture designed to keep resources productive.
- Third, make data optimization part of the engine, not an afterthought.
Getting data into storage quickly is not enough if it becomes expensive and slow to query afterward. Yukon continuously compacts, sorts, and organizes incoming data, maintaining optimized Apache Iceberg and Delta Lake tables directly in customers' cloud storage.
Data becomes queryable within a minute of arrival, while the engine continues to optimize it for downstream workloads.And because Yukon is built around open formats, customers retain ownership of their data and can use the analytics and query engines they already rely on.
This is a fundamental principle of our approach: the engine should do the hard work, without forcing customers into an entirely new ecosystem.
Performance That Changes the Economics of Data
A new architecture only matters if it produces meaningful results.In our benchmark workloads, Yukon demonstrated 7 to 10 times faster ingestion than Apache Spark and Flink on equivalent compute resources. To maintain a 60-second freshness target, it required approximately 7 to 8 times less compute. Compaction benchmarks showed roughly 8 times faster execution than Spark.
These results reflect specific tested workloads, but they demonstrate the potential of rethinking the architecture instead of simply adding more machines.
We are also seeing this efficiency translate into real production environments. At Rise, a global AdTech platform, Eon's data engine processes more than 900 billion events and over a petabyte of data every day, delivering query-ready Apache Iceberg tables with sub-minute freshness. Compared with a comparable Apache Flink deployment, the solution uses approximately 10 times less compute, with reported infrastructure costs up to 30 times lower.
At this scale, performance isn't just about completing a task faster. It fundamentally changes what organizations can afford to do with their data.
When processing becomes dramatically more efficient, businesses can ingest more information, preserve richer history, maintain fresher datasets, and support AI applications that previously would have been prohibitively expensive.
We believe the next generation of data engines will be measured not just by how fast they process data, but by how much useful data they can deliver for every dollar spent.
Building the Data Foundation for What Comes Next
We see Yukon as more than a faster ingestion engine. The next evolution of data infrastructure is about transforming raw information into something both humans and intelligent agents can understand and use.
As Yukon processes data, it can continuously produce aggregations, column statistics, quality signals, and contextual information describing what the data contains and how it behaves.
Through this context, AI agents can move beyond simply discovering tables and guessing what their columns mean. They can work with fresher information, understand its structure and semantics, and make better-informed decisions.
Equally important, Yukon supports fully agentic data workflows. Agents can create, configure, and manage data pipelines, connect new sources, and continuously maintain the data lake itself. They can keep tables clean, fresh, and efficient by managing data quality, compaction, and optimization as data changes, without requiring people to orchestrate every step.
This creates a powerful new model: some agents build and maintain the data foundation, while others query that same continuously optimized lake to reason, make decisions, and take action. The infrastructure becomes an active part of the AI workflow, not simply a passive place to store information.
This connects directly to our broader vision at Eon: a world where enterprise data is continuously available, organized, and ready to power intelligence.
We believe the next wave of applications will require an infrastructure foundation that combines massive scale, real-time processing, open data, and rich contextual understanding, all at a fraction of today's cost.
Building that foundation requires more than optimizing existing technology. It requires the willingness to rethink how data systems should work.
Our experience protecting exabytes of information taught us what truly matters at scale. Our engineering approach gave us the opportunity to design a new architecture around those lessons. And the emergence of AI has made the need for that architecture more urgent than ever.
We built Yukon because we believe the future of data processing should be dramatically faster, simpler, fully agentic, and more economical than it is today.
