Store, process, manage and share your data anywhere, anytime, anyhow. Hybrid data platforms for the modern era with limitless scale, hyper-performance and native resilience.
A modern data platform (MDP) is a collection of tools, capabilities and data sources that, when combined, enable an organisation to become a data-driven organisation. Given the scale and complexity of data today, it’s no longer enough for a modern data platform to just process and store data; it must move and adapt faster than ever to keep up with the diversity of data and its users.
An MDP should enable self-service capabilities for users’ data management whilst simplifying the complexity associated with the extract, transform and load (ETL) process. Modern data ingestion tools enable data from different sources to be ingested into the core data storage layer. The data storage and processing layer has evolved from data warehouses to data lakes and now to data lakehouses that combine the best of both.
It is now possible to build a next-generation data platform architecture that solves the challenges of scaling. Disaggregated cloud-native or cloud-like on-premises architectures scale linearly to exabyte proportions, while pioneering next-generation levels of flash efficiency and economies make it possible to realise the dream of massive scale without performance compromise. No spinning disks. No tiers. No limits. Structured and unstructured data services at any scale, eliminating islands of data infrastructure with a single platform built to deliver multi-protocol support.
Data platforms need to leverage cutting-edge cloud, compute, storage and fast networking technologies to unleash the value of your data. Eliminating the need for multiple storage options and data copies across workflows, reducing operational complexity, enhancing pipeline efficiency and increasing GPU utilisation.
A single data platform should support all popular data access methods, including the POSIX-compliant file system, NFS, SMB, S3, CSI for Kubernetes, and GPU Direct Storage (for direct data movement between GPUs and storage). The use of NVMe and NVMe Storage Fabrics can provide a significant increase in the performance of MDPs.
Thanks to advancements in deep learning, all industries are at the start of a new AI-powered era. IT organisations now face new, unprecedented data and storage challenges as AI becomes woven into the enterprise computing agenda.
Modern AI processors require extraordinary bandwidth and the IOPS necessary to randomly walk through massive training data sets. These AI workloads overwhelm conventional NAS technologies, causing application bottlenecks. AI pipelines cannot prefetch random read operations, therefore they require a highly scalable and affordable all-flash, file and object platform that allows you to cost effectively run at petabyte and exabyte scale.
Today, most workloads can be run anywhere, so organisations can choose where to run each application based on which location delivers the best performance, scalability, agility, security and cost outcomes. Organisations must decide, therefore, which workloads to put in the cloud and which to keep in on-premises datacentres. The challenge today is not choosing between the cloud or on-premises but finding the optimal place to run each workload.
A hybrid cloud data platform is a cloud-native, software-defined storage solution for next-generation workloads like artificial intelligence and machine learning that supports large-scale data collection and processing on high-performance computing infrastructures. The platform provides a unified experience, allowing organisations to configure storage to meet their specific requirements without forcing a choice between cloud and on-premises solutions. It offers performance, agility and scalability, no matter where the workload runs, enabling organisations to deliver applications and data where they provide the best experience for their customers.
High-Performance Data Analytics (HPDA) is at the crossroads of traditional business analytics, high-performance computing (HPC) and artificial intelligence (AI). HPDA workloads now apply predictive models for deeper insights. Success is measured by time-to-results.
Use cases are utilising HPDA to gain competitive advantage, from detecting fraud and anomalies to affinity marketing and business intelligence.
Data platforms using modern storage architecture can handle intensive I/O workloads with the highest demands and latency-sensitive applications at petascale, improving HPDA outcomes.
Data platforms can provide one globally accessible data set, optimising everything for maximum performance and productivity at any scale. Cross-site collaboration with other users around the globe is possible in real time at near-zero latency.
Storage, analytics, disaster recovery and security are all integrated. Distributed data consolidation makes data accessible and durable without making copies, thereby driving down costs. Immutable data protection ensures cyber-attacks don’t damage files and the system quickly reverts to previous file versions via immutable data captured by snapshots. Hybrid cloud NAS can leverage the cloud for the single source of truth whilst ensuring secure, global access throughout the enterprise.
Kodata. A next-generation VAD specialising in both proven infrastructure solutions and emerging frontier technologies. We help our channel partners deliver the resilience, performance, and security their customers demand. Kodata. Value-Added Distribution for today and the quantum future.A wealth of experience with a fresh approach. Hybrid infrastructure solutions for the modern era.
If you would like to hear more about how we can help you meet the challenges of today and the quantum future, then please don’t hesitate to get in contact.
Kodata. Value-Added Distribution for today and the quantum future.