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AWS DEVOPS
Driving innovation in Healthcare IT
Streamline manual processes to reduce high mean time to restore (MTTR), security risks, and task repetition. Utilize the AWS DevOps Competency Program with CitiusTech to implement enterprise-grade DevOps strategies to optimize resource utilization tailored to your organization's size, industry, and tech stack.
CONNECT WITH US
Accelerate Healthcare DevOps with AWS to drive modernization across enterprises
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SERVICE AREAS
Bringing innovative, secure applications to market faster
Build End to End Cloud Native Continuous Delivery Ecosystem to improve engineering agility.
Assess and improve your security posture and compliance across the stack.
Embed Healthcare cloud controls and automated software governance.
Case Study
Helped an independent pharmacy solutions organization setup its AWS cloud infrastructure foundation
To best serve ~20M members, the pharmacy organization needed to ensure that the new cloud infrastructure for their Value-based care analytics solution is stable, secure, available and scalable. Considering the criticality of the application, CitiusTech leveraged ECS Fargate which helps reduce the manual efforts required to choose server types or decide when to scale your clusters. Further, the application deployment is automated with AWS Code Build and AWS Code Pipeline avoiding manual intervention. To ensure minimized application downtime, blue/green deployment has been integrated. AWS principals and AWS Cloudwatch have also been implemented within the infrastructure.
Case Study
Helped a leading biotechnology organization by enabling self-service, unified, scalable, and regionally highly available analytics platform.
With an increasing number of users and data on their analytics platform, the users often encountered system performance challenges with slowness or unavailability. CitiusTech leveraged AWS Elastic Kubernetes Service to manage the master nodes and use AWS Elastic Load Balancer for controlling the ingress traffic along with other tools like Prometheus and Splunk for monitoring and logging. Auto scaling groups are established with the cluster auto scaler, facilitating node scaling in accordance with container quantity and cluster load. This automatic scaling process adjusts dynamically based on the workload. Even with an increased workload, the users no longer experience any system degradation.
