Genomics Data Engineering and MLOps Platform

Deploying a Scalable and Repeatable Genomics Data Engineering and MLOps Platform for a Biotech Startup

Our client, a biotech startup focused on genomics research, required a robust infrastructure to process and analyze large volumes of DNA and RNA sequencing data. Our goal was to design a platform that enabled rapid setup for new datasets and automated scalable storage and analysis on AWS.

Solution Overview

Our solution utilizes AWS services such as AWS DataSync, AWS Lambda, AWS Step Functions, Amazon S3, and Amazon QuickSight to build a scalable genomics data platform. This platform focuses on automation, scalability, and cost efficiency in line with the AWS Well-Architected Framework.

Solution Architecture

Diagram: Workflow architecture of the genomics data engineering platform on AWS, illustrating the end-to-end data processing pipeline from ingestion to visualization.

Solution Components and Workflow

1. Data Collection and Ingestion with AWS DataSync

• Automated Data Transfer: Fast and secure data transfer from on-premises to AWS S3.
• Cost and Performance Optimization: DataSync optimizes the transfer of large data volumes, reducing costs.
• Monitoring and Auditing: Ensures all data transfers are logged and monitored.

2. Initial Data Processing with AWS Lambda

• Serverless Processing: Minimizes compute costs by activating only when data arrives in S3.
• Data Validation: Ensures high-quality data processing, reducing errors.
• Data Encryption: Uses AWS KMS for encryption at rest and SSL/TLS in transit.

3. Secondary Data Processing with AWS Step Functions

• Data Cleaning and Transformation: Complex workflows for data readiness.
• Modular Pipeline Design: Flexible and scalable processing stages.
• Automated Resource Management: Dynamically manages computing resources.

4. Storing and Organizing Processed Data in Amazon S3

• Structured Data Storage: Data stored in a well-defined S3 structure for easy access.
• Data Lifecycle Management: Implements policies for cost-effective long-term storage.

5. Data Visualization and Insights with Amazon QuickSight

• Customizable Visualization: Provides dynamic dashboards for data interaction.
• Real-Time Data Insights: QuickSight’s SPICE engine facilitates efficient data exploration.

6. Delivery of Insights to Lab Professionals

The final stage involves delivering processed data insights through QuickSight dashboards, enabling real-time genomic analysis.

Outcome and Benefits

• Rapid Deployment: Quick setup and processing capabilities reduce deployment time.
• Dynamic Cost Optimization: Minimizes costs through effective use of AWS services.
• Enhanced Data Security and Compliance: Implements stringent security measures for sensitive data.
• Scalability and Flexibility: Easily scales with increasing data volumes to support growing research demands.
• Repeatable Deployment: Uses Infrastructure-as-Code for easy replication of the platform.

This AWS-based solution enhances the client’s research capabilities and positions them for future innovation in bioinformatics.