Introducing HyperGraph, database & compute platform
Data is the lifeblood of modern business. From daily operations to big-picture strategies, everything revolves around it. But there's a catch: the more data you have, the harder—and more expensive—it gets to manage. You might not notice it right away, but as your business grows, so do the inefficiencies lurking in your data systems.
And that’s the problem we’re tackling today. The more your data system stretches beyond its natural scaling limit, the less efficient it becomes. In economics this is called diseconomies of scale, and it can turn a once-profitable asset into a costly burden if you’re not paying attention.
Diseconomies of Scale
“The decrease of efficiency in the making of a product by producing more of it. That is, diseconomies of scale occur when a company increases its output for a product such that it increases the cost per unit of the product. For example, assume that labor costs at a factory are constant as long as the factory produces between 100,000 and 500,000 units per month. If the factory produces more than 500,000 units per month, it may have to hire more workers, which would increase the cost per unit. It is easier for smaller companies to fall into diseconomies of scale because they have less control over their costs; indeed this can cause many smaller companies to be at a significant competitive disadvantage. See also: Economies of Scale.
Farlex Financial Dictionary. © 2012 Farlex, Inc. All Rights Reserved “
As both the amount of data and the workload on a system increase, the cost per operation rises, meaning businesses get less efficiency as they grow.
As businesses expand, so does their data. And as their user base increases, the workload increases as well, often exponentially. This leads to a major issue—data systems have a relatively short lifespan before they turn from enabling profit to driving cost and risk.
Common Challenges of Scaling Data Systems
Some of the struggles businesses face with this include:
Slowing data systems that lead to unresponsive user experiences, frustrating users and leading to attrition.
Poor system performance that stops key business functions from working properly, and hurts operations.
Data systems that have already hit their limit to scaling end up blocking the creation of new features, putting the business at a disadvantage in the market.
Rising infrastructure costs that eat into profits, especially for popular features that users expect to have. Even though costs increase, you can’t remove these features without losing customers.
The bigger the data, the harder and riskier it becomes to change or move the data or the system. For mature businesses, large data systems often support significant revenue, creating increased friction between the business's aversion to risk existing revenue, and product and engineering's need to make changes to the system to help it scale to meet rising demand.
Under the hood, as the size of the data in a system grows, so does its management cost, leading to an increase in the cost-per-unit of operation as well. And as businesses optimize for growth, this characteristic locks them into a cycle of ever increasing investment in their data management, as it transitions from profit driver to cost center.
The technical challenge is therefore to design a data system that can handle unlimited growth with constant unit economics. That is, without the cost-per-operation increasing as the data size grows. In other words, the cost of storing, fetching, and processing data should stay the same, no matter how large the data in the system grows. If a business finds that data management is affordable and profitable on a small or medium scale, those same cost benefits should hold true even when the data and workload become massive. The goal is to keep the cost per operation constant, regardless of how much the data grows.
HyperGraph: Solving the Diseconomy of Scale Problem
Our solution to the data systems' diseconomy of scale problem is HyperGraph, a data platform built from the ground up to handle unlimited scalability while keeping the cost per unit of work constant. HyperGraph is inspired by many different technologies and fields, including FPGAs, neuromorphic computing, graph theory, and causal set theory, as well as our backgrounds in physics & math, and building high-performance and massive scale data systems with low latency requirements.
At its core, HyperGraph builds on top of a local-first graph execution runtime paired with an infinite graph traversal algorithm. Treating executions as finite traversals over an infinite graph is what allows HyperGraph to handle computations that go beyond the capacity of any single node and seamlessly scale to massive amounts of data without the performance degrading impact of global coordination.
In addition to the HyperGraph runtime, we are developing an easy-to-use data-system-as-code environment that will let businesses quickly build the exact data system they need and adjust it as their needs change—without the hassle and expense of risky data migrations, and without the need for a large data engineering organization to support it.
Stay Informed About HyperGraph
If you're intrigued by the potential of HyperGraph and want to stay ahead of the curve as we continue to develop, now is the perfect time to get involved! While HyperGraph is still in its early stages, you can be among the first to receive updates, insider insights, and exclusive early access. Sign up for our newsletter or follow us on social media to stay connected—we’d love to keep you informed and get your feedback as we build the future of scalable, cost-efficient data systems!
