Challenges of cloud
infrastructure for big data

Building, operating and managing cloud infrastructure in a scalable and efficient way remains a challenge for big data teams.

Big data teams are slowed down by cloud operational tasks instead of executing on core data initiatives.
The cost of environments for experimentation, model building, testing and other activities is growing as big data applications scale.
DevOps teams want to support big data but are stymied by manual processes and lack of experience with big data software.
Cloud native technologies like Kubernetes enable the scalability and agility that the cloud promises, but requires significant time and expertise to configure, manage and monitor.

Teams focus on big data applications while Spot handles scaling and optimization of cloud infrastructure

Hands-free cloud experience for data teams
Support big data teams and increase productivity with automated services that setup, configure and monitor cloud infrastructure.
Visibility yields actionability
Improve resource utilization and efficiency by tracking actual consumption and right-sizing workloads.
Automation and continuous optimization
Improve resource utilization and efficiency with automated optimization mechanisms that scale, provision and manage cloud resources based on specific big data job requirements.
Capture cost savings
Take full advantage of cost-saving cloud services like spot instances with enterprise-level reliability.
Ocean for Apache Spark

Containerized application deployments

Serverless Spark applications running on Kubernetes.

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Traditional application deployments

Manage infrastructure for EMR clusters running big data frameworks like Apache Spark and Hadoop, integrating with existing tools like Airflow.

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