- Independently scale compute and storage using local SSD and HDD pools optimized for big-data
Accelerate Time to Value
- Single platform to run multiple big data applications with high performance: Hadoop, Spark, Cassandra, Splunk, MongoDB
- Self-service deployments within 10 minutes for developers and R & D teams
- Built-in templates in app store for quick and easy deployment
- Reduce TCO by 50% by eliminating physical silos, resource sharing and 90% lower operational costs
Big Data Analytics
We are living in a data and analytics driven economy. Big-data solutions like Hadoop, Spark, Cassandra have significantly lowered the cost of data retention and analytics. This is providing a significant competitive edge to businesses that are storing more data and doing analytics to extract business value. The longevity and success of a business in today’s market is driven by how they choose to collect, store, analyze and act on their data. Businesses need a flexible infrastructure to run big-data workloads, while controlling their operational and capital costs.
There are two models of deploying big-data infrastructure: physical and virtual. Typically physical infrastructure is used for production deployments in many cases, as it is not as elastic and flexible as virtual deployment. A virtualized environment for big-data allows data scientists to create their own Hadoop, Spark or Cassandra clusters and evaluate their algorithms. These clusters need to be self-service, elastic and high performant. IT should be able to control the resource allocation to data scientists and teams using quotas and role based access control.
Why ZeroStack Cloud Platform
The ZeroStack Cloud Platform provides a virtualized environment where data scientists can spin up multiple big-data application clusters on demand and scale as needed. The platform also allows optimal utilization of resources and performance guarantees to run these applications. ZeroStack has unique local storage capabilities to avoid double replication of data both at infrastructure and application-level, specifically designed for big-data application use case. IT can allocated projects with specific quotas to one or more users to allow them to work independently.
ZeroStack supports several big data applications via the built-in zApp Store. This built-in app store offers pre-built application templates that enable you to deploy big data applications with ease. Some of the example templates include multi-node Hadoop clusters, Apache Spark clusters, Cassandra clusters, Apache Storm clusters, and HDFS. Users can “import” these templates to their ZeroStack private cloud in a single click and then deploy them. These templates have configuration options that allow for storage, networking, and compute optimization as needed for a given environment. Users can also create and upload their own custom big data application templates to the app store.
Resource sharing using virtualization
Eliminate physical silos and consolidate multiple big-data applications on a single platform. 50% lower CapEx due to resource sharing, high utilization and 90% lower Opex with self-service.
Faster Time to Value
Provide self-service deployment of big data applications like Hadoop, Spark, Cassandra to your development and R&D teams. They can deploy applications within minutes.
Control consumption of resources using projects with quotas and policies governed by IT. Monitor over-commitment and add capacity using built-in capacity planning indicators. Get insights to improve efficiency and performance based on actual application stats and machine learning. Optimize your capacity using long term analytics.
Scale on Demand
Scale your infrastructure to meet compute performance and data growth. Build with one server at a time and grow based on actual usage.
Eliminate operational complexity
With cloud-based monitoring and analytics in Z-Brain, you do not need any local infrastructure monitoring solution. IT teams do not need any certificates or expertise to operate infrastructure. Reduce total cost of ownership (TCO) by 50% while cutting operational complexity by 90%.