Listen to this articleBelow is our recent interview with Ken Grohe, Chief Revenue Officer of Stellus Technologies:
Q: What’s the best way to describe Stellus Technologies?
A: Stellus provides Hyperscale Throughput for the Digital Enterprise.
The Stellus Data Platform is a SaaS-based system built for Cloud, Core and Edge infrastructures. Stellus created the industry’s first Key-Value over Fabrics system. The Stellus Data Platform combines Key-Value over Fabric with high-throughput key-value stores and algorithmic data locality — to deliver consistent, fast, highly scalable parallel data access performance. Stellus is designed for the most demanding data-intensive workloads and applications such as cognitive AI, machine learning, high-speed microscopy, genomic sequencing, and data streaming applications.
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Q: How will Stellus Technologies revolutionize the industry?
A: Stellus is revolutionizing the way companies manage their unstructured data. It’s not just about the speed in which we do it. It’s about what that hyperscale speed enables. Stellus improves time-to-result in the work that is transforming the world and saving lives.
Let me give you a few examples.
For Media & Entertainment, Stellus customers can run 32 simultaneous streams across 32 screens of 4k uncompressed data. 800% faster! That power gets movies out of post-production editing weeks or even months faster.
We recently broke the Single Client Throughput World Record as well at 28.7 GB per second (the previous record was 22 GB per second).
For Life Sciences, Stellus allows high-speed microscopy to be utilized 24×7, with real-time access. Legacy solutions produced a huge disparity between reads and writes, which caused bottlenecks that forced researchers to wait for the delay from data collection to data analysis – and ultimately only use the machines for 4 hours each week. Stellus is making the power of high-speed microscopy a reality in the research community.
For Genomics, only Stellus is powering more than 500 genomic sequences per day. Legacy solutions can handle only 150 or less. The ability to modify gene sequences is the direct link to finding the cure to diseases such as cancer.
Q: Why is now the time for a technology solution like Stellus?
A: Technology and business buyers want to decouple performance from capacity. With legacy solutions, companies are buying capacity they might not need in order to get to the performance they require. And it’s a waste of money and time. Stellus changes that.
To use a car analogy, decoupling is the ability to buy a 2-seater car. And then later and only when you need it, upgrade that car to a 4-door with a large trunk for storage/capacity and then upgrade from a 4-cylinder to an 8-cylinder — right in your driveway, without needing to buy an entirely new car.
Wouldn’t that revolutionize car ownership if this type of decoupling flexibility existed like it does with Stellus for high performance Networked Attached Storage?
Q: What is key-value store or Key-value over Fabrics — and why is it important?
A: Hyperscalers like Google, Amazon, Facebook and LinkedIn had to start with a brand new slate when they were building a scalable system that could serve billions. And they built it using key-value store architecture because only a key-value store-based file system can handle that demand. Legacy storage architectures rely on block and file-on-block technologies that were great for 15-20 years ago before SSD and Flash – but for today’s modern environments where companies depend on massive amounts of unstructured data to deal with machine learning and AI and other processes to make strategic business decisions — Stellus is the only answer as it is the only system using this innovative Key-Value over Fabrics architecture.
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Q: What can we expect from Stellus in the future?
A: Stellus delivers an innovative Key-Value over Fabrics data system to market at a time when businesses desperately need a faster, more scalable way to manage growing amounts of unstructured data.
Stellus allows businesses to capture, store, and process increasingly large data sets, and it makes data accessible faster and more efficiently than anything else available today and by decoupling capacity and performance in a near-linear trajectory.
