MapR delivers support for Amazon Elastic Container Service for Kubernetes; offers scalable, secure data platform for seamless cloud deployments



MapR Technologies, provider of data platform for AI and analytics, announced on Thursday that the MapR Data Platform now supports Amazon Elastic Container Service for Kubernetes (Amazon EKS). MapR previously announced persistent storage for containers to enable the deployment of stateful containerized applications.

With Amazon EKS, MapR makes it easy for organizations to adopt and manage their data seamlessly on-premises and on Amazon Web Services (AWS), ensure end-to-end security, and protect containerized applications with advanced replication, snapshots, and mirrors.

Amazon EKS automatically manages the availability, scalability, and scheduling of containers. With MapR, organizations can retain the disaggregation of scaling compute independent of their storage, without having to worry about over subscription. MapR also secures containers from data access vulnerabilities through wire-level encryption and a full end-to-end set of access, authorization, and authentication features.

With MapR, organizations can delete, create, and move containers and still be able to access the data regardless of where their compute process is running. MapR provides data protection and highly available data access through replication and advance snapshot and DR features; data is distributed across cloud instances, availability zones, on-premises and edge resources while not impacting the portability of the containers.

MapR Data Fabric for Kubernetes provides persistent storage for containers and enables the deployment of stateful containers. It addresses the limitations of container use by providing easy and full data access from within and across clouds and on-premises deployments. Now stateful applications can easily be deployed in containers for production use cases, machine learning pipelines, and multi-tenant use cases.

As organizations adopt containers at a larger scale, and as they move containers as a deployment model to production, other aspects such as storage, monitoring, and performance becomes essential as well. MapR Data Fabric for Kubernetes provides several features that will assist organizations in their journey with containers.

MapR integration with the Kubernetes storage plugin allows for MapR volumes to be mounted for access by containers. As new containers are deployed, data volumes can be created and retained, even when containers are deleted. If containers are moved across environments, the MapR global namespace provides access to the data, independent of where the containers reside. Applications can be synchronized and updated with a unified view and without disruption. Data stored in MapR can benefit from the full-fledged advantages of high availability, security, data protection, and disaster recovery.

MapR Data Fabric for Kubernetes, combined with the capabilities of the MapR Converged Data Platform, is beneficial to organizations desiring to run workloads in containers. Essentially all enterprise applications can be run as containers on MapR. An exhaustive set of MapR capabilities, such as high availability, security, and high performance, enables organizations to deploy quickly.

Users can share applications and add new users  with less infrastructure implication on MapR. The MapR small footprint edition enables applications running on edge clusters to be deployed in containers. MapR offers a consistent, reliable platform, integrated with Kubernetes for continuous development and integration. Its machine learning applications are portable and smart, and can be deployed using MapR Streams for real-time feed of data and machine learning pipelines.

“Data agility is essential for next-gen analytics and advanced applications,” said Jack Norris, senior vice president, data and applications at MapR. “The robustness of MapR combined with the agility of Amazon EKS enables enterprises to quickly build a flexible and secure production environment for large scale AI and machine learning.”

 

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