Enterprises and Cloud Providers have several challenges when dealing with Analytics (ie. unstructured data). First, is timely processing (ie. indexing, searching, etc.) of large data volumes that meets the needs of the business. Second, is storing the data which may be done in the form of a data lake. Businesses are trying to solve these problems while minimizing their spend on the infrastructure to do so.
Each data service has its own operational practices, but hiring specialists or buying support agreements for each is prohibitively expensive.
Containers solve the infrastructure differences between clouds and on-prem data centers for compute, but don't address the challenges of running stateful apps in different environments.
Developers want self-service, but you can't risk giving up control of corporate policies like security, data retention, backups, and more.
Analytics pipelines, much like CI/CD, are often implemented to automate the ingest and indexing of data resulting in more timely actionable insights to the business. The Diamanti ML Platform provides the flexibility for data scientists to accelerate model training with GPUs and deploy CPU-optimized trained data models and any other workloads to the same cluster.
Diamanti multi-zone clusters allow Kubernetes nodes to be distributed across different availability zones (failure domains), ensuring applications can achieve.
Manage multiple Kubernetes clusters across bare metal and public resources from a single control plane, allowing the application-driven infrastructure decisions for both stateless and stateful applications.
Accelerate development cycles, shrink application footprints for more flexibility, and improve scalability and reliability. All while reducing TCO and improving performance by up to 3x.
Enhance security, availability, and scalability to bring production-ready enterprise applications to Kubernetes.
Analytics pipelines, much like CI/CD, are often implemented to automate the ingest and indexing of data resulting in more timely actionable insights to the business. The Diamanti ML Platform provides the flexibility for data scientists to accelerate model training with GPUs and deploy CPU-optimized trained data models and any other workloads to the same cluster.
With Diamanti’s highly available storage system, data is readily accessible for model training as well as to run analytics to draw insights. This avoids the back and forth movement of massive datasets between the online processing system and of-the-cluster storage, saving significant time for data scientists.
Supercharge any cloud-native big data or AI/ML project with guaranteed Diamanti QoS. Leveraging Diamanti for large-scale workloads, enterprises are able to :
Run popular data management and processing services on Kubernetes
Diamanti works with every major Kubernetes platform and supports the most common data service providers, ensuring that your architecture and ecosystem can gain the full support of a purpose-built solution that is secure, reliable, and future proof.
With Diamanti, your Splunk is more secure, faster and easier to manage. Read this eBook to learn how running Splunk on the Diamanti platform is an exceptional fit for your business.
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Purpose-built Kubernetes software with unified storage and networking layers for multi- and hybrid-cloud environments.
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