TRE ALTAMIRA Chooses Weka on Amazon Web Services to Unlock Innovation and New Product Development
Customer reveals how the high-performance file system proved to be a game-changer for their data-intensive geospatial workflow
SAN JOSE, Calif. – Dec 11, 2018 – WekaIO, an innovation leader in high-performance, scalable file storage for data intensive applications, today announced TRE ALTAMIRA, a world leader in measuring ground and structural movement from space, is using Matrix on Amazon Web Services (AWS) to process their dataset of satellite imagery for their customers. TRE ALTAMIRA chose the Matrix file system for its high-performance storage capabilities on AWS, which has eliminated production capacity limits, significantly reduced costs, and removed barriers to product innovation.
TRE ALTAMIRA uses satellite radar technology to measure ground and structural movement for clients in sectors ranging from oil and gas, to civil engineering, and geo hazards. Its technology has played a key role in such high-profile rail-transit projects as the Grand Paris Express and Canada Line. Processing the 4TB datasets required to provide such information to clients, requires significant processing power and high-performance storage; requirements that limited the company’s production capacity to 30 analyses per week. Thus, TRE ALTAMIRA found its growth capped at this number and its ability to innovate similarly hamstrung by the lack of available resources to test and develop promising new algorithms.
“The situation was untenable, and we needed an urgent solution to solve the fast-growing resource constraints. Help came in the form of a recommendation for WekaIO Matrix on AWS. It was astonishing how easy the implementation and the integration was versus trying to build and manage our own Lustre® file system using Ubuntu instances,” said Alessandro Menegaz, IT Manager at TRE ALTAMIRA. “The improvements we got in terms of performance and costs savings have been great too. Recently we completed one of the most demanding analysis that we have ever made in less than two weeks with Matrix. In contrast, with the Lustre system we built, a similar analysis a year ago took two months and we spent three times as much. Plus, the WekaIO support is unparalleled.”
Time-to-market has also improved exponentially, as Matrix eliminated limitations on the number of concurrent analyses. An average execution now takes just 12 hours, regardless of how many orders are being processed simultaneously, which has simplified the sales process by unlocking the ability to offer guaranteed delivery times. Finally, TRE ALTAMIRA says WekaIO Matrix on AWS has opened new areas of innovation for its business; enabling it to revisit products and services that were previously discarded for want of resources.
“TRE ALTAMIRA is the perfect example of a data-intensive use case whose explosive potential could only be fully tapped by the combination of Matrix on AWS, our cloud-native storage solution that enables high-performance computing at massive scale and elasticity,” said WekaIO CEO, Liran Zvibel. “Here you have a customer that began immediately reaping tremendous returns on its investment from the get-go—faster time-to-market, better cost-management, new product innovation potential. We couldn’t have hoped for a better outcome with the power of the Matrix solution on AWS.”
For more information, read the case study on WekaIO’s website: https://www.weka.io/wp-content/uploads/2018/08/TREALTAMIRA-CS-W01CS201809.pdf
WekaIO Matrix is available on the AWS Marketplace.
WekaIO helps companies manage, scale and futureproof their data center so they can solve real problems that impact the world. WekaIO Matrix™, the world’s fastest shared parallel file system and WekaIO’s flagship product, leapfrogs legacy storage infrastructures by delivering simplicity, scale, and faster performance for a fraction of the cost. In the cloud or on-premises, WekaIO’s NVMe-native high-performance software-defined storage solution removes the barriers between the data and the compute layer, thus accelerating artificial intelligence, machine learning, genomics, research, and analytics workloads.
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