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Autonomous cloud infrastructure that adapts to big data
Give data experts their time back and give applications scalable, flexible and cost-efficient cloud infrastructure.
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.
Data experts, not cloud experts
Big data teams are slowed down by cloud operational tasks instead of executing on core data initiatives.
Cloud cost management
The cost of environments for experimentation, model building, testing and other activities is growing as big data applications scale.
Manual processes
DevOps teams want to support big data but are stymied by manual processes and lack of experience with big data software.
Complex management
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.
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.
Visibility yields actionability
Improve resource utilization and efficiency by tracking actual consumption and right-sizing workloads.
Capture cost savings
Take full advantage of cost-saving cloud services like spot instances with enterprise-level reliability.
Containerized application deployments
Serverless Spark applications running on Kubernetes.
Traditional application deployments
Manage infrastructure for EMR clusters running big data frameworks like Apache Spark and Hadoop, integrating with existing tools like Airflow.