Cloudian Partners with WEKA to Deliver High-Performance, Exabyte-Scalable Storage for AI, Machine Learning and Other Advanced Analytics

Cloudian announced the integration of its HyperStore object storage with the WEKA Data Platform for AI, providing high-performance, exabyte-scalable private cloud storage for processing iterative analytical workloads. The combined solution unifies and simplifies the data pipeline for performance-intensive workloads and accelerated DataOps, all easily managed under a single namespace. In addition, the new solution reduces the storage TCO associated with data analytics by a third, compared to traditional storage systems.

Advanced Analytics Workloads Create Data Storage Challenges

Organizations are consuming and creating more data than ever before, and many are applying AI, machine learning (ML) and other advanced analytics on these large data sets to make better decisions in real-time and unlock new revenue streams. These analytics workloads create and use massive data sets that pose significant storage challenges, most importantly the ability to manage the data growth and enable users to extract timely insights from that data. Traditional storage systems simply can’t handle the processing needs or the scalability required for iterative analytics workloads and introduce bottlenecks to productivity and data-driven decision making.

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Cloudian-WEKA Next Generation Storage Platform

Together, Cloudian and WEKA enable organizations to overcome the challenges of accelerating and scaling their data pipelines while lowering data analytics storage costs. WEKA’s data platform, built on WekaFS, addresses the storage challenges posed by today’s enterprise AI workloads and other high-performance applications running on-premises, in the cloud or bursting between platforms. The joint solution offers the simplicity of NAS, the performance of SAN or DAS and the scale of object storage, along with accelerating every stage of the data pipeline from data ingestion to cleansing to modeled results.

Integrated through WEKA’s tiering function, Cloudian’s enterprise-grade, software-defined object storage provides the following key benefits:

• High Performance – Run concurrent workloads while eliminating compute cluster bottlenecks and reducing processing times.
• Exabyte Scalability – Grow deployments on demand, from terabytes to an exabyte without disruption, achieving the flexibility and elasticity of the public cloud within a private data center or hybrid cloud model.
• Enterprise-grade Security – Protect data with encryption in flight and at rest, integrated firewall, RBAC/IAM and SAML access controls, and certification with the most rigorous regulatory requirements, such as Common Criteria, FIPS and SEC Rule 17a-4(f).
• Resiliency – Achieve high data durability with the option to protect and distribute data using replication or erasure coding, thereby eliminating the need for a separate data backup process.
• Multi-tenancy – Provision multiple users on shared infrastructure without compromising security.
• Cost-effective – Save on storage costs, as the solution runs on standard x86 hardware with local NVMe SSDs.

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“As organizations increasingly employ AI, ML and other advanced analytics to extract greater value from their data, they need a modern storage platform that enables fast, easy data processing and management,” said Jonathan Martin, president, WEKA. “The combination of the WEKA Data Platform and Cloudian object storage provides an ideal solution that can seamlessly and cost-effectively scale to meet growing demands.”

“When it comes to supporting advanced analytics applications, users shouldn’t have to make tradeoffs between storage performance and capacity,” said Jon Toor, chief marketing officer, Cloudian. “By eliminating any need to compromise, the integration of our HyperStore software with the WEKA Data Platform gives customers a storage foundation that enables them to fully leverage these applications so they can gain new insights from their data and drive greater business and operational success.”

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