Robin Systems is simplifying the management and deployment of big data workloads by adding a Kubernetes layer to its platform to deal with stateful container applications running on premises or in a public cloud. This will allow management using standard Kubernetes, which was originally focused on dealing with stateless container management.
The Kubernetes infusion is part of Robin Systems’ Hyperconverged Kubernetes Platform. The platform supports Kubernetes-based management for big data, stateful databases, artificial intelligence (AI), and machine learning applications.
Robin Systems CTO Partha Seetala said that the update allows users to tap into existing workflows and use the same tools for an application management layer overseeing compute and storage. He noted that the Robin Systems platform provides a self-service, application store-like experience to the management of big data, database, AI, and machine learning use cases.
Seetala said the platform also tackles issues with using Kubernetes to consolidate workloads on hardware. He explained that this usually results in “noisy neighbor” issues that prevent the storage stack from being able to offer guaranteed service level agreements (SLAs). The Robin Systems control layer allows DevOps and IT teams to have direct control over quality of service, SLAs, and lifecycle management.
Kubernetes is designed for stateless applications. This means that it was not created to handle data storage. This is not a problem for cloud native web services like a web server or a front-end web user interface that do not depend on the local container storage for the workload.
However, stateful applications are services that save data to storage and use that data to run the application. These include databases and complex applications like big data and AI use cases that involve large-scale data processing, data science, and machine learning (ML). Basically these are workloads that currently use platforms like Spark, Kafka, Hadoop, Cassandra, and TensorFlow.
“Kubernetes is great for running stateless applications but struggles with big data or stateful applications,” Seetala explained. “But more companies are looking for ways to manage more of their storage needs with Kubernetes.”
This has led to a robust business for storage vendors developing stateful appendages that can plug into a Kubernetes-managed container deployment to handle storage needs. Seetala said there are more than two dozen storage-related Kubernetes vendors that are part of the Cloud Native Computing Foundation (CNCF) landscape map.
One of those is Portworx, which earlier this year partnered with Hewlett Packard Enterprise (HPE) on a reference configuration that uses Kubernetes to offer enterprises a quick way to deploy and manage stateful container workloads. The reference configuration combines HPE’s Synergy composable system as the basis for running Portworx’s PX-Enterprise storage platform using Kubernetes as the container orchestrator and scheduler.
More recently, BlueData launched an open source project to tackle the challenges of deploying and managing distributed stateful applications using Kubernetes. The moves are targeted at large-scale applications like analytics, data science, machine learning, and deep learning applications for AI and big data use cases.
Seetala said Robin Systems sees itself as a competitor to Portworx, but different from BlueData because Robin Systems offers a storage stack instead of just scripts.
“We are a completely packaged hyperconverged platform that is managed by us and allows us to go beyond just something like Hadoop,” Seetala said. “We handle the long-term provisioning and management.”
$17M Series B
Robin Systems this week also closed on $17 million in Series B funding, which brings its total VC haul to more than $69 million. The latest funding round was led by USAA Ventures, with participation from Hasso-Plattner Ventures, Clear Ventures, and DN Capital.