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DDN: Data Management Solution for Data-Centric AI, Analytics, and Real-Time Insight

Any data, any speed, any scale, anywhere.

DDN provides data management solutions (hardware, software, and data) for data-centric artificial intelligence (AI), analytics, data engineering, and real-time insight. Their hardware underpins large portions of the public internet as well as private networks in data centers around the world.

The 41st IT Press Tour had the opportunity to meet with Kurt Kuckeni, VP Marketing, James Coomer, SVP Products, and Meredyth Jensen, CMO of DDN.

Corporate investments in AI hit record highs in 2021:

  • Microsoft invest $1 billion in an artificial intelligence project co-founded by Elon Musk

  • Pandemic shines a spotlight on big data and AI in life sciences and healthcare

  • Our latest mobility start-up and investment tally show the industry invested $120 billion in the last 24 months as it prepares for the years to come

  • Huawei to invest $1 billion on car tech it says surpasses Tesla

These investments are shifting the boundaries of traditional computing and analytics.

  • Data scientists have created a new need to source new data science talent, maintain currency and up-to-date skillsets in a rapidly changing software environment.

  • Data sources have created a new need to ingest high volumes of data from broad sources through a variety of ingestion methods at rates well beyond traditional computing requirements

  • Data processing has created a new need to implement large-scale GPU environments to bring the parallelism needed for training and inference in real-time

  • Data governance has created a new need to label, track, and manage that data (forever) and share that data across your organization with the right security policies

Data is the source code of AI, data is imperative to AI, and storage is imperative to AI.

“I have a very simple statement for you; Nvidia uses DDN” – Manuvir Das, Head of Enterprise Computing, Nvidia

DDN is powering the next era of data-centric computing. Since 1998 DDN has built the world’s fastest supercomputer storage environments applying end-to-end parallelism to data movement. Today, the universe of supercomputing has expanded rapidly to incorporate AI, advanced analytics, and cloud computing. The era of serial data is coming to a close and the next era has begun.

"DDN is the defacto name for AI storage in high-performance environments." – Marc Hamilton, Nvidia.

DDN pioneered accelerated data at scale to tackle challenges ordinary storage cannot.

"The UKs most powerful supercomputer has gone live, with potential to transform AI-based healthcare research” – ZDNet

With 400 petaflops of AI performance, the Cambridge-1 supercomputer gives healthcare researchers in the U.K. a powerful new tool to take on medicine’s toughest problems. Cambridge-1 is powered by DDN A3I.

“Managing our at-scale data needs requires fast ingest, optimized processing and reduced application run times.” – Kris Howard, Recursion.

DDN A3I is up to 20X less costly and raises the possibilities for accelerating the pipeline with new levels of AI capability with the ability to run AI workflow concurrently, continuously, all in one place. It simplifies data management with universal, unified, secured access for distributed AI applications.

Addressing Data Challenges

  • Streamline concurrent workflows for data scientists. Supports the need to fail fast and iterate.

  • Scale economically with super fast ingest for data sources. Tight AI integration optimized performance-proven at scale for data processing.

  • Secure no-silo approach with advanced workload insight for data governance.

Any scale solutions from a single appliance to improve data governance by quickly delivering AI and analytics infrastructure to a risk-averse large securities services company.

For mid-sized clusters helping the U.S.’s largest children’s hospitals accelerate cancer research and treatment through optimized AI data management and performance.

For full-blown hyper-scale supercomputing enabling hyper-scale natural language processing at one of the world’s largest enterprise software companies.

Converging Customer Requirements

Customers need:

  • A solid digital transformation strategy enabled by the right partner with enterprise and AI expertise.

  • More accurate real-time insight from data for a competitive edge.

  • Simple deployment of flexible, distributed data management solutions that address all parts of the data lifecycle.

  • Drive down the cost of the infrastructure and simplify it.

Case Studies

AIaaS transforming cancer care with managed services. DDN is providing a service for precision oncology seeking to conquer cancer through proprietary blood tests, massive datasets, and advanced analytics. They are using bioinformatics and HPC to analyze sequence genome data liquid biopsies. Their data challenge requires performance and reliability as previous systems experienced tremendous hardware failures. The DDN solution is a parallel file system storage as a fully managed service providing evergreen support, capacity, and performance on demand, in an OPEX model.

Faster statistical analysis results through simplified scaling. Simplifying data management for a global financial services and venture firm. The mathematics and programming-centric financial organization brings a scientific approach to financial products. Metadata-heavy applications challenged existing NFS-based storage performance. 50+PB datasets presented a management challenge with existing systems. The DDN solution provides efficient performance from fewer systems which are easier to manage and scale in the future.

Transforming research data storage. From a management and maintenance nightmare to a universal resource for a large California university life sciences research organization that supplies storage and data management to a wide variety of projects and requirements. The prior system was homegrown and self-supported. Scale, performance, and stability needs outgrew current capabilities. The DDN solution provided a simplified and reliable infrastructure with a roadmap for growth and added capability. The result was easy access for researchers with no need to change workflows. There are plans to further accelerate research with GPU clusters.

Challenges > Solutions

  • Cost and complexity of data ingest, distribution, analysis of PBs of machine-generated data. > Simple and complete platform for each stage of the data management lifecycle.

  • Rapid data growth has weakened the ability to meet the needs of data scientists and researchers. > Accelerates the discovery and time-to-market with higher performance, scalability, and efficiency.

  • Insufficient CPU and GPU performance for data-intensive workloads. > Deep application understanding, experience in streamlining deep learning (DL) workflows for greater productivity.

Data stages require container support for SAN and NAS, block storage for reference databases, nearline data archive, and landing for high data-rate instruments. With CSI drivers for block and file, low-latency block for FC and iSCSI protocols, high-capacity, cost-effective NAS, fast, native SMB support.

Optimized end-to-end enterprise AI data architecture. Complete AI data management from a single vendor. Integrated data management, easy access interactive usage for user pre- and post-processing, compilations, and file editing, enterprise protection data protection features, total data lifecycle management to cost-efficient deduplication on the compression-enabled archive.

AI optimized and unlimited scaling, standard enterprise features for home directories and containers, space-efficient, fast flexible backup, and archive.

Leadership in simplicity at any scale. Install the fastest systems in the world in under 15 minutes with DDN’s new management framework (EMF) simplified command and control, simplified software lifecycle management, health and performance visualization.

More efficiency, more acceleration – hot nodes. Hot nodes accelerate deep learning with intelligent integration into the AI compute environment. Transparent to users. Brings new efficiencies for the whole environment (GPU, network, and storage).

Smarter storage helps you deliver a better service. Other storage systems can’t see which workloads are accessing and exercising the file system while DDN enables users to see applications and users in real-time.

End-to-end solutions from the leaders in at-scale. Complete new IntelliFlash portfolio to complement DDN A3I. One partner to help manage your entire data infrastructure. Best in class speed, capacity, and enterprise features/protocols. Data management integrating backup and archive, cloud sync.

New right-size solutions for flexible scaling and tiering. DDN scales up and scales out exactly as you need it. Start with 250TB NVMe @RU appliances that grow to any capacity. Scale linearly with 90GB/s NVMe building block appliances. Scale-up with best price/PB up to 6.4PB per appliance (and up to 18PB in Q3 2022).

2022 and Beyond

DDN has the ideal set of solutions, technologies, and the global presence to help organizations be more successful in all things having to do with data. Expertise in deploying the right solutions for the right use cases at scale. A robust and comprehensive product portfolio. End-to-end global data management solutions. Safe and optimized digital transformation expertise. Edge and core, cloud and on-prem, performance, and archive solutions.

Tintri current and emerging market opportunities. VM or microservice allow APIs to connect with any AIOPS customers are using, K8s workflow already supports Tanzu K8s today add more as more embrace containers, microservices.

Transforming our customer’s ability to extract value from data. Deliver the best experience more orchestration, even simpler management of at-scale environments. Help our customers deliver a better service. Take even more advantage of DDN’s ability to see the data center and workloads. Help customers grow. Even more scalability and media flexibility. Help our customers accelerate data without risk. Tighter, deeper integrations into AI frameworks and AI systems to eliminate risk from at-scale solutions.


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