Analytics

Draw deeper value from business data

The Challenge

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.

Many Apps

Each data service has its own operational practices, but hiring specialists or buying support agreements for each is prohibitively expensive.

Many Environments

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.

Uncontrolled Self-service

Developers want self-service, but you can't risk giving up control of corporate policies like security, data retention, backups, and more.

The Diamanti Solution

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. 

High Availability and Multi-Zone Deployments

Diamanti multi-zone clusters allow Kubernetes nodes to be distributed across different availability zones (failure domains), ensuring applications can achieve.

Hybrid Cloud Flexibility

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.

Fast Track Your Digital Transformation

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.

Data Security

Enhance security, availability, and scalability to bring production-ready enterprise applications to Kubernetes.

Gain Insights Into Your Environment

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.

Readily Accessible, Always Active

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.

Produce New Efficiencies With Analytics

Supercharge any cloud-native big data or AI/ML project with guaranteed Diamanti QoS. Leveraging Diamanti for large-scale workloads, enterprises are able to :

  • Deploy GPU and CPU-targeted workloads to the same cluster
  • Maximize resource utilization and return on investment
  • Experiment, train, and run production ML models on a single platform
  • Reduce footprint by >50%

Data Services

Run popular data management and processing services on Kubernetes

Data Analytics & Diamanti

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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