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Innowise enhanced an advanced data management platform for precision medicine diagnostics, streamlining the analysis of diverse healthcare datasets to accelerate patient-treatment matching and provide critical insights for drug development.
Our client is a pioneering company in precision medicine diagnostics. Their product serves as a critical intermediary between medical facilities, patients with conditions like cancer or heart disease, and pharmaceutical companies developing treatments for these diseases. The advanced data management solution aggregates and analyzes diverse datasets, including lab test results, patient outcomes, medicine efficacy to precisely match patients with appropriate treatments and clinical trials, while also providing valuable insights to pharmaceutical companies for drug development and targeted patient population identification.
Detailed information about the client cannot be disclosed under the provisions of the NDA.
The company faced significant inefficiencies in their data processing pipelines and environment setup, hampering their ability to effectively aggregate, process, and analyze critical diagnostic test data from multiple sources. These inefficiencies led to delays in data availability for both data engineers and end-users, potential data quality issues, and suboptimal resource utilization in their AWS infrastructure.
The client also experienced challenges with adding new users and managing permissions for existing users within the AWS environment. The Innowise team consisting of DevOps engineers and data scientists was entrusted with these tasks.
Our experts led a comprehensive overhaul of the client’s software to implement a multi-faceted solution.
Our DevOps engineers redesigned the infrastructure workflows to improve its efficiency and scalability. We performed profiling of the existing data pipelines to identify gaps and then optimized data structures and formats to reduce redundancy and improve processing efficiency. To further speed up data transformation and analysis, the experts implemented parallel processing techniques. We also improved and refactored code to enhance its maintainability. These efforts resulted in a streamlined, high-performance data pipeline system.
We optimize the utilization of AWS cloud infrastructure by right-sizing instances, and implementing auto-scaling. We also applied Infrastructure-as-Code principles using Terraform to automate provisioning and management of cloud resources. Docker helped to containerize the data processing environment for consistency across development, testing, and production. A CI/CD pipeline was established to automate code integration, testing, and deployments. We also set up automated testing for the environment to timely catch configuration issues.
We implemented AWS IAM best practices to enhance user and permission management. This included creating policies based on the principle of least privilege and setting up multi-factor authentication (MFA) for all IAM users. We optimized EC2 instance types based on workload analysis and set up CloudWatch alarms for proactive monitoring. Furthermore, to mitigate security risks, we developed automated scripts for user management and permissions.
Back end
Python
Cloud platform
AWS
Infrastructure as a Code
Terraform
Containerization
Docker, Amazon EKS
Database
AWS RDS
Security and access management
AWS IAM, Secret Manager
Monitoring and logging
AWS Cloudwatch, Grafana, Prometheus
CI/CD
GitHub Actions
Compute service
AWS EC2
Our project to enhance the precision medicine data management platform followed a structured approach, ensuring each aspect of the solution was aligned with the client’s needs.
We examined the client's data processing pipelines and AWS infrastructure, pinpointing inefficiencies and areas for improvement.
We restructured the system to enhance data handling, scalability, and security within AWS.
Using Python and related tools, we improved back-end processes, data structures, and implemented parallel processing techniques.
We created Terraform scripts to streamline AWS resource management.
We containerized the data processing environment with Docker and set up automated integration, testing, and deployment pipelines.
We evaluated data processing speed, accuracy, system reliability, and IAM security measures.
1
Project Manager
2
DevOps Engineers
2
Data Scientists
1
QA Engineer
The implementation of our solution led to significant improvements in our client’s’ data management capabilities.
35%
reduction in data loading times
29%
decrease in AWS cloud computing costs
Having received and processed your request, we will get back to you shortly to detail your project needs and sign an NDA to ensure the confidentiality of information.
After examining requirements, our analysts and developers devise a project proposal with the scope of works, team size, time, and cost estimates.
We arrange a meeting with you to discuss the offer and come to an agreement.
We sign a contract and start working on your project as quickly as possible.
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