Prefect

Data engineers and data scientists often find themselves bogged down by "negative engineering"—the defensive code written to ensure that data pipelines run reliably, handle failures gracefully, and manage dependencies correctly. Instead of focusing on delivering value through data analysis and modeling, teams spend a disproportionate amount of time building and maintaining the infrastructure to orchestrate these workflows. This friction slows down innovation and creates fragile systems that are difficult to debug and scale.

Prefect addresses this challenge by providing a modern dataflow automation platform that empowers teams to build, run, and monitor resilient data pipelines using the tools they already know and love. By treating "code as workflows," Prefect allows developers to write standard Python code and automatically upgrades it with orchestration features like scheduling, retries, caching, and distributed execution. This approach eliminates the need for complex, rigid DAG (Directed Acyclic Graph) definitions and boilerplate code, enabling engineers to focus on the logic of their applications rather than the mechanics of their execution.

Our platform offers a hybrid model that combines the security of on-premise execution with the convenience of a managed cloud control plane. This ensures that your data never leaves your infrastructure while giving you full visibility and control over your workflows through a user-friendly dashboard. With features designed for observability, such as real-time logging and error tracking, Prefect helps organizations of all sizes—from startups to Fortune 50 enterprises—transform their data engineering practices, reduce pipeline errors, and accelerate the delivery of data-driven insights.