Dagster enables health and life science companies to build a scalable data platform for analysis, machine learning, and AI use cases on large, disparate datasets
Without it, data stays siloed, reporting slows down, and cross-functional teams are left guessing.
Handling large, diverse datasets like genomic data and health records requires bespoke tools and complex transformations. Different teams are using disparate tools, making collaboration difficult and a unified view of data impossible.
Integration and deploying trained ML models into production workflows is challenging. Coupled with a lack of observability, teams have difficulty gaining insights into the state of data pipelines, data lineage, and monitoring data processing.
Ensuring data handling practices adhere to regulatory requirements is difficult and slows down velocity, especially when there are no internal standards for data pipelines. Teams are using their own tools without a standardized workflow.
Processing vast amounts of data efficiently requires complex distributed computing environments, which often are difficult for domain experts to use. Bottlenecks are more common than collaboration as teams rely on a small platform team to be able to operate effectively.
Dagster provides a single unified platform for life science and health tech companies to do everything from clinical research, drug discovery, development, and evaluation, to AI-driven research on patient data.
Keep every team in sync
Connect and orchestrate data across tools and systems with clear visibility and end-to-end lineage.
Add data validation where it matters
Run automated checks across datasets before they move into reporting or submissions.
Build standardized pipelines for governance
Create a platform that everyone can leverage with their own tools, but under a common governance framework.
BenchSci reduced computation costs by optimizing data pipelines and only materializing assets with clear benefits. They reduced data errors through improved observability under Dagster’s unified control plane.
Read the full case study
Signficant cost reductions
By computing only assets that need to be materialized, BenchSci saved on compute costs.
Fewer errors, faster research
Improved observability led to faster research and fewer errors.
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