How to build a Data Warehouse for Life Science with Amazon RDS?

Why Life Sciences Need a Data Warehouse?

Imagine a biotechnology company running multiple clinical trials. Each trial generates terabytes of patient data, lab reports, EHRs, and sensor logs. Without a central, organized repository, drawing meaningful insights, such as patient response trends or adverse event signals, becomes chaotic, risky, and time-consuming.

A data warehouse built on Amazon RDS allows life science companies to consolidate disparate data sources (lab systems, IoT devices, EMRs, CRMs) into a scalable, cost-effective environment. This centralized store enables more effective reporting, streamlined regulatory submissions, and AI/ML-based insights.

Why Amazon RDS?

Amazon RDS simplifies the setup, operation, and scaling of databases in the cloud. It supports standard engines like PostgreSQL, MySQL, SQL Server, and Oracle, all commonly used in the life sciences industry. With built-in automation for backups, patching, high availability, and security, RDS lets your data teams focus on science, not servers.

Benefits for Life Sciences:

• HIPAA-eligible & compliant: RDS helps meet healthcare and life science compliance requirements.
• Automated backups & snapshots: Critical for audits and reproducibility of research.
• High availability (Multi-AZ deployments): Ensures your data warehouse is always accessible, even during peak loads or outages.
• Encryption at rest and in transit: Safeguards sensitive patient and research data.

How to Build a Life Sciences Data Warehouse with Amazon RDS?

1. Define Your Data Sources: Start by identifying the systems feeding into your warehouse, such as LIMS, EHRs, instrument data, and even CSV files from lab reports.
2. Choose the Right RDS Engine: PostgreSQL is often the go-to for life sciences due to its support for complex queries, extensions like PostGIS (for location data), and integration with analytical tools.
3. Design Your Schema: Use a star or snowflake schema to optimize for analytics. Organize your fact tables (e.g., patient visits, lab results) and dimension tables (e.g., time, location, researcher).
4. Data Ingestion: Use AWS Glue, Amazon S3, or custom ETL pipelines to clean, transform, and load data into RDS. You can even use AWS Data Migration Service (DMS) for near real-time replication from on-prem systems.
5. Query and Analyze: Once data is in, use tools like Amazon QuickSight or connect RDS to BI tools like Tableau or Power BI for dashboards and deep analytics.

Case Study: Synaptics Achieved Scalable, Cost-Effective Data Modernization with Amazon RDS

While life sciences organizations have unique regulatory and operational challenges, they share common data hurdles with companies in other industries, including the need to reduce costs, scale efficiently, and modernize legacy systems. One illustrative example is Synaptics, a global leader in human interface solutions, which successfully modernized its data infrastructure by migrating from Oracle to Amazon RDS for PostgreSQL. Their journey offers valuable lessons for biotech and pharmaceutical firms looking to modernize and centralize their data environments.

The Challenge: Legacy Systems Blocking Agility and ROI

Synaptics operated Atlassian JIRA and Confluence on an aging on-premises stack: Tomcat on RedHat Enterprise Linux, backed by Oracle 11g. This setup was expensive to maintain, inflexible to scale, and heavily reliant on Oracle licensing. Business teams needed better performance, scalability, and a leaner TCO (Total Cost of Ownership). Most critically, the IT team was under pressure to migrate without any downtime—a situation familiar to any life sciences organization running 24/7 lab systems or clinical trial data repositories.

The Solution: Zero Downtime Migration to Amazon RDS PostgreSQL

To address these needs, Mactores partnered with Synaptics to assess and implement a robust migration strategy. The recommended solution was to move their Oracle-based database to Amazon RDS for PostgreSQL using the AWS Database Migration Service (DMS) and Schema Conversion Tool (SCT).

This approach was chosen over simpler export/import methods due to the database’s size (360GB) and the need for minimal disruption. Using DMS, Mactores enabled Change Data Capture (CDC), which allowed real-time data sync between Oracle and RDS PostgreSQL during the transition. This ensured that Synaptics could achieve zero downtime.

Key AWS tools and services used

• Amazon RDS for PostgreSQL: Provided a scalable, managed database environment.
• AWS DMS: Enabled live replication and continuous sync.
• AWS SCT: Automatically converted schema and stored procedures while accounting for Oracle’s case-insensitive structure vs PostgreSQL’s case-sensitive one.
• DB Solo & SolarWinds: Third-party tools for post-migration data verification and performance stress testing.

Results That Life Sciences Companies Can Learn From

The outcome was a 70% improvement in ROI and complete data consistency with zero downtime.

What does this mean for life sciences companies?

• From Lab to Cloud Without Disruption: If your organization runs LIMS, EHRs, or R&D systems on-prem, this proves that even large, complex databases can be transitioned to the cloud with zero impact on day-to-day operations.
• License-free Future: Life sciences companies often grapple with expensive, proprietary database systems. Moving to Amazon RDS PostgreSQL offers cost savings without sacrificing performance.
• Scalable, Global Collaboration: Just like Synaptics’ globally distributed teams relied on Atlassian 24/7, your researchers across continents can benefit from a cloud-native infrastructure that supports seamless collaboration and real-time data access.

Mactores, Your Technology Partner

With deep expertise in AWS services, including Amazon RDS, AWS DMS, and the Schema Conversion Tool, Mactores has helped organizations like Synaptics and many others navigate complex migrations, streamline operations, and unlock real-time data insights. Whether you're moving from Oracle, SQL Server, or legacy on-prem systems, our team ensures zero disruption, complete data fidelity, and a future-ready architecture tailored to the demands of life sciences.

From genomics to clinical trials, and from regulatory reporting to global research collaboration, we help you modernize with confidence. When you're ready to transform your data warehouse into a competitive advantage, Mactores is your trusted technology partner.

FAQs

• Why should life sciences organizations consider Amazon RDS for their data warehouse?
  Amazon RDS offers a fully managed, scalable, and cost-efficient way to handle structured data, crucial for life sciences workloads such as clinical data, lab results, and regulatory reporting. It eliminates infrastructure overhead and ensures high availability, compliance, and performance.
  
• Can Amazon RDS support zero-downtime migration from legacy databases?
  Yes. Using AWS Database Migration Service (DMS) with Change Data Capture and Schema Conversion Tool (SCT), organizations can migrate large databases from systems like Oracle to Amazon RDS PostgreSQL without downtime, ensuring continuous access to critical data.
• How can Mactores help life sciences companies implement Amazon RDS solutions?
  Mactores specializes in cloud-native transformations and has experience migrating enterprise workloads to AWS. From planning and architecture to execution and optimization, Mactores ensures a secure, compliant, and disruption-free transition to Amazon RDS for life sciences data platforms.