Medidata’s journey to a modern lakehouse architecture on AWS !

On the AWS Big Data Blog, there’s a detailed post titled “Medidata’s journey to a modern lakehouse architecture on AWS”, co-authored by a Principal Engineer at Medidata. The post explains how Medidata replaced its older batch-ETL, multi-pipeline architecture with a unified, real-time lakehouse built on AWS. Amazon Web Services, Inc.
Key improvements from this shift: moving from scheduled batch jobs (which introduced latency and complexity) to streaming ingestion using Apache Flink + Amazon Managed Kafka (MSK) + Apache Iceberg on AWS — resulting in drastically reduced latency (from days to minutes) and enabling a consistent, single source of truth for data consumers. Amazon Web Services, Inc.
The unified lakehouse harnesses the AWS metadata catalog (via AWS Glue Data Catalog), which simplifies data governance, security, and access control (via IAM), removing the need for multiple custom access-control layers across disparate systems. Amazon Web Services, Inc.

Building on this modern architecture, Medidata launched Medidata Clinical Data Studio — a unified data-management and analytics solution for clinical trials. This leverages the lakehouse underpinnings to enable efficient data integration (even from non-Medidata sources), automated data reconciliation, and better data-quality management across trials. Dassault Systèmes+1
The shift to a modern data architecture is described as a foundational step in empowering Medidata’s life-sciences and clinical-trial platform to scale globally — improving how data is collected, processed, analyzed, and shared for research, trials, and regulatory compliance. Amazon Web Services, Inc.+2Medidata Solutions+2

The adoption of a “lakehouse” architecture is part of a bigger trend across enterprises and life-sciences organizations: unifying data lakes and warehouses to support analytics, AI/ML, and efficient data governance — often a prerequisite for modern digital transformation and compliance-heavy domains. ETCIO.com+2InfoWorld+2
Recent innovations from AWS — notably Amazon SageMaker Lakehouse — reflect this trend: AWS now offers managed lakehouse services that integrate data across S3 data lakes and Redshift warehouses under a unified, Iceberg-compatible format. Amazon Web Services, Inc.+2AWS Documentation+2

📝 Why This Matters — Impact of Medidata’s Lakehouse Architecture

Faster, real-time data access: Instead of waiting for periodic batch jobs, data consumers now get near-real-time access — enabling more timely analytics, reporting, and decision-making for ongoing clinical trials.
Reduced operational complexity: Fewer custom pipelines and disparate systems mean lower maintenance burden, fewer failure points, and improved reliability — which matters critically in regulated life-sciences data environments.
Stronger data governance & security: Centralizing metadata and access control via the lakehouse makes it easier to enforce permissions, audit data usage, and comply with regulatory requirements — a must-have in clinical research and patient data handling.
Scalability & flexibility: With a unified architecture, Medidata can more easily ingest, store, and process large volumes of data — including real-time streams, EHRs, sensor data, and trial results — without needing separate warehouses or bespoke pipelines.
Enabler for advanced analytics & AI: With data curated, cleaned, and readily available in one place, downstream teams and applications (e.g. in clinical analytics, risk management, AI-backed insights) can work more efficiently and reliably.