Databand is a system for tracking, monitoring, and scheduling data pipelines.
We help data teams manage and optimize their processes for data preparation, ML model training, testing, and deployment.
Ease of Use
Pipelining tools are a good solution for adding structure and reuse in data science development. However, most pipeline solutions available today are not easy to work with - a big issue in highly iterative ML work. Databand simplifies the process of building, scheduling, and understanding DAGs and pipelines so that data engineers can debug faster and data scientists are empowered to build on their own.
Databand provides deeper visibility into the flows of data running through your pipelines so that you can quickly understand what your data looks like, how it is changing, and how it will affect your models downstream.
Being able to recreate every run of a pipeline (or experiment) is critical in fast-moving machine learning teams to prevent wasted efforts. Databand automatically saves all artifacts and context from every runs so everything is totally accessible and reproducible.
Databand helps teams track the health and performance of their pipelines by validating that data flows, runtimes, events, and other attributes are what they should be, so that it's easy to pinpoint exactly where issues are coming from and cut through the noise.