Data Engineering

TetraScience's Data Engineering capability transforms raw, siloed scientific data locked in vendor-proprietary formats into purpose-engineered, liquid, compliant, and large-scale data optimized for advanced analytics and AI/ML applications. Designed for life sciences enterprises, it enables organizations to unlock meaningful value from their scientific data by streamlining data preparation and automatically converting raw instrument and experimental outputs into a standardized format known as Tetra Data.

Tetra Data serves as the essential, atomic building block for capitalizing on the power of analytics and AI in science. TetraScience brings deep, long-term expertise in scientific data and best practices in purpose-engineering data, ensuring that organizations can move from raw data to actionable insights at scale.

What Defines Tetra Data

• Compliant: Ensures data integrity and full traceability of datasets, with audit trails and versioning to support compliance with 21 CFR Part 11 and GxP regulations and guidelines.
• Liquid: Transforms static, inaccessible, and siloed data into liquid data that flows seamlessly across instruments, applications, departments, and organizations.
• Purpose-engineered: Breaks free from proprietary data formats by harmonizing data into an open, vendor-agnostic, and AI-ready format complete with robust scientific taxonomies and rich ontologies.
• Large-scale: Brings siloed data together into the large-scale datasets required for meaningful AI-driven insights.

Scientific Lakehouse Architecture

• Natively supports AWS Athena, Redshift, and Snowflake for flexible cloud data warehousing.
• Leverages Delta Lakehouse architecture, providing a foundation for Databricks and other data analytics and processing solutions.
• Supports multi-modal data consumption ranging from data discovery and analytics to high-performance computing and AI model training.

Enterprise-Grade Data Management

• Consistent data governance: Combines the capabilities of a data lake, a data warehouse, and more through a data lakehouse architecture capable of handling raw, complex, semi-structured, and unstructured data assets, enforcing holistic and consistent data governance.
• No data duplication and federated data access: Avoids data redundancy and enables seamless data access across multiple domains within an enterprise, allowing organizations to incorporate the Tetra Scientific Data and AI Platform into existing enterprise data platforms and warehouses without data duplication or tedious ETL processes.

Key Use Cases and Resources

• Continuously updated Tetra Data models covering a growing range of scientific instruments and data types.
• Harmonized data to improve utilization of lab resources across the enterprise.
• Demonstrated ability to eliminate time-consuming data cleansing for global biopharma organizations receiving data from multiple contract research organizations (CROs) by automatically harmonizing data at scale.
• Streamlined regulatory preparation through robust audit capabilities, as validated by enterprise customers including global biopharma regulatory science teams.

The Tetra Scientific Data and AI Platform is built for enterprise deployment, integrating with leading cloud and analytics infrastructure including AWS, Snowflake, Databricks, and existing enterprise data warehouses. It supports regulatory compliance requirements such as 21 CFR Part 11 and GxP, making it well-suited for life sciences organizations navigating complex data governance and compliance landscapes.