Lamin vs The Modern Data Company

<p>Lamin is designed to help you manage large-scale biological data with ease. It offers a lineage-native lakehouse supporting various bio-formats and integrates data and metadata through registries and ontologies. You can query, trace, and validate datasets using simple code, allowing you to maintain data integrity without losing administrative control over your infrastructure. Whether you are working with Python, R, or SQL, Lamin unifies your metadata and datasets using relational sheets synced with storage, making project and change management intuitive.</p> <p>With Lamin, you gain the ability to track data lineage across notebooks, scripts, pipelines, and reports, ensuring transparency of data origins and usage. It enforces consistency with schemas and annotations, supports fine-grained permission control, and works across major cloud platforms and local infrastructure. This platform is ideal for teams that need to build a long-term organizational memory by compiling data, models, and reports into a unified, queryable system that grows and learns over time.</p>

<p>The Modern Data Company provides DataOS, a powerful platform designed to upgrade your data stack by delivering AI-ready data products that come with built-in context, governance, and activation features. It helps your teams launch new data use cases faster by creating reusable data products that adapt to changing needs without rework or delays. You gain control with embedded policies, ownership, and traceability, ensuring your data environment stays compliant and manageable without slowing down your workflow.</p> <p>With DataOS, you eliminate hidden costs caused by data complexity by improving delivery speed, adding usage observability, and making ROI traceable to maximize the value of every data investment. This platform seamlessly integrates into your existing infrastructure without requiring a full rip and replace approach. It is recognized by industry experts and included in Gartner’s Magic Quadrant for Data Integration, making it trusted by thousands of data professionals aiming for efficient and governed AI-driven data operations.</p>