Today's AI/ML and analytical workloads can be bursty and unpredictable. Provisioning infrastructure for worst-case (maximum load) compute scenarios is costly and unnecessarily increases administrative overhead. Kubernetes solves for this, partially, by letting organizations orchestrate and spin up containers as compute needs arise.
Yet challenges persist. In particular, organizations are left with the difficult task of manually integrating their applications with Kubernetes constructs like Namespaces and Operators. Additionally, segregating and isolating resources within Kubernetes in today's de facto multi-tenant environment is far from automatic. Finally, organizations must make difficult choices as they try to leverage and persist data across tenant containers.
Key Capabilities in This Release* MapR accelerates the separation of compute and storage with the following key capabilities:
Key Technical Integrations MapR has introduced a number of key technical integrations that simplify this experience:
*GA is expected in Q2 2019.