Scale Different — How we Reimagined Data Storage to Give Data Freedom

Marc Fleischmann
Jan 20 · 9 min read

What’s wrong with data storage?

Before starting Datera in 2013, Nicholas Bellinger and I had contributed the block storage subsystem to Linux (“Linux-IO”), which was adopted by numerous companies, including Pure Storage, Red Hat (now IBM) and Google. Linux-IO eventually became an industry standard, and Nic and I became somewhat notorious as the original software-defined storage dudes. Life was good.

While the industry took great advantage of our open source software to replace proprietary storage hardware with less-proprietary storage hardware (and called it “software-defined”), we thought they missed the point: Mapping rigid architecture of the past into software is not enough to meet the demands of digital transformation. Getting stuck with the hardware you start with simply no longer works.

We imagined a service-centric architecture to orchestrate data anywhere, while scaling continuous availability and predictable performance across private and public clouds. We knew hyperscale architecture had the innate adaptability we wanted, but we needed to systematically reimagine it to address its inherent challenges. If we could harness this adaptability to let users change their intent as they go, we could free their mind from the mental slavery of trying to anticipate future storage needs. Thus, Datera was born.

Why hyperscale?

Swarms are adaptive, monoliths are not. A swarm of starlings is infinitely adaptable and resilient, dinosaurs are the antithesis of adaptive — and extinct. If implemented correctly, everything else flows from this basic concept.

For instance, Google adopted swarm design almost twenty years ago. They build their data centers from thousands of commodity servers, and use autonomous distributed software to orchestrate them into coherent swarms. Now known as “hyperscale,” this architecture is transforming how IT is designed and delivered, putting IT as we know it under existential pressure — the Jurassic IT Era is coming to its end.

A self-organizing swarm

The promise of hyperscale is to converge diverse hardware resources into one coherent swarm with incredible adaptability and scalability. Its implementation, however, is riddled with hard practical challenges like node heterogeneity, data gravity, data consistency, combinatorial reliability and availability, operational complexity, performance and scalability cliffs, and so on, plus fundamental physical realities like time, distance and latency.

To harness the power of hyperscale, we assembled an amazing interdisciplinary team around our founding architects Claudio Fleiner (a hyperscale wizard), Raghu Krishnamurthy (an automation thought leader) and Bill Rozas (a brilliant computer architect), who created a game-changing core technology portfolio and product that solves the innate conceptual challenges with hyperscale.

This allowed us to make hyperscale storage work, and finally bring its benefits to a wide spectrum of workloads, traditional and cloud native, including the most demanding mission critical enterprise applications. Effectively, we delivered the first enterprise-grade software-defined storage.

True software-defined storage

Over the next few weeks, I’ll be publishing a series of four blogs that explain Datera’s key architecture tenets, why they are fundamental to make hyperscale storage work, and how they help transform IT.

In this series of four blogs, I’ll explain how we addressed fundamental hyperscale problems by systematically rethinking basic concepts, based on first principles, in three key categories: infrastructure model, automation model and hybrid cloud model, and how those confluence to transform the traditional IT operating model, from systems to scalable services — thereby capturing significantly more value across the entire data and system life cycle.

1. Infrastructure model

Our key infrastructure tenet was to make hyperscale frictionless. What good is hyperscale if it can’t adapt rapidly and continuously, deliver inspiring and predictable performance, scale seamlessly, rebalance quickly among a rich spectrum of endpoints, and so on?

Data infrastructure model

So, at the inception of Datera, we spent significant time reimagining hyperscale without its innate friction points. As a result, we created a hyperscale infrastructure model that delivers:

The result is a uniquely flexible data services platform that can independently orchestrate data services and data across all of its endpoints. Live data services mobility and live data mobility lay the foundation to achieve data freedom across private and public clouds.

2. Automation model

Now that we created an infrastructure model that makes hyperscale frictionless and truly scalable, effectively creating long invisible arms across the datacenter, we turned to our next key tenet: automation. What good is frictionless hyperscale if it requires slow humans to work it?

Datera automation model

So, our hyperscale automation model is driven by applications — effortless, instant, invisibe. Applications know best what they want:

Containers further escalate the speed, scale and elasticity pressure on infrastructure — they consume infrastructure as a service that is continuously delivered. In that model, Kubernetes, Mesos and their brethren replace manual operations with application-driven automation of compute swarms — and Datera is to data as Kubernetes is to compute.The impact of this new automation model is hard to imagine without experiencing it.

Remember when the iPhone overnight made flip phones feel so 1990s? Experiencing Datera is a similar watershed moment — it makes traditional storage feel antiquated. What made the key difference for the iPhone? Apps. The iPhone is driven by apps, not by humans using a dialpad, just like Datera is driven by applications, not by humans using a keyboard.

3. Hybrid cloud model

Now that we have achieved data freedom across the data center, we can parlay it into a hybrid cloud model that lets us scale it across private and public clouds.

Our application-driven automation model allows describing the behavior of data in invariant application profiles, or storage blueprints, that we automatically adapt to tenancy and roles. Storage blueprints seamlessly expand the behavior of data beyond the box — they allow a “data broker” to make data portable, scalable and hybridizable.

Our complementary frictionless infrastructure model provides scalable live data mobility — effectively implementing a “data exchange” across private and public clouds.

Datera hybrid cloud model

As workloads are moving to the edge, the data center is evolving into a meta data center, and clouds get abstracted behind brokerage layers, we can provide the scaled data broker and data exchange to achieve data freedom from the intelligent edge to cloud.

4. Operating model

Our innovative storage infrastructure and automation models allow us to fundamentally decouple storage consumption from deployment, and continuously broker between them. Now we can rethink the storage operating model from rigid point-in-time systems to continuously composable and scalable data services that empower both consumers and operators to independently optimize their needs:

We can uplevel this concept to refactor the entire IT value creation chain, from planning through obsolescence, to deliver transformational simplicity and efficiency:

Datera operating model

Effectively, we bring the cloud experience to enterprise data storage, and let you free your mind (and data!) to focus on creating business value:

The result is a comprehensive data foundation for the modern software defined data center. Together with enterprise partners that have a global brand, reach and support, efficient supply chains, and equipment financing, we can deliver game-changing operational value to enterprise customers — not just for hyperscale, but for any scale.

Scale different

Customers are looking to replatform their IT to cloud in order to increase business agility and reduce technology risk. Public clouds have enormous OpEx elasticity, which makes failure cheap but success expensive, and they lock customers in with captive data services. Thus there is a universal need for data services that converge public cloud simplicity and elasticity with private cloud control and efficiency, to create multi-cloud optionality.

To meet this need, we have reimagined storage to bring the cloud experience to data. We have rethought storage to scale and orchestrate data across the constant flux of capabilities and consumption, technology innovation and obsolescence cycles, and across private and public clouds — to scale across space and time, driven by current and future application intent. We have envisioned an “eternal” data services continuum that combines software-defined simplicity with enterprise ‘abilities.

As a result, we created mission critical software-defined storage that is future-proof for the demands of digital transformation — a 24x7 lights-out data continuum that scales data freedom from the intelligent edge to cloud. Because the people who are crazy enough to think they can reimagine storage at scale, are the ones who do.

Please visit us at, or tweet me at @MarcFleischmann.

Marc Fleischmann

Written by

Technology entrepreneur, blogger and visionary in #cloud and #autonomous systems, CEO background, @DateraInc founder.