Prefect

Prefect is a Python-first workflow orchestration platform designed to make it easy to convert scripts and functions into production-ready data pipelines. Instead of forcing users into DSLs or static DAG definitions, Prefect enables workflows to be written in pure Python using simple decorators for tasks and flows. That Pythonic approach supports type hints, async/await, and familiar development tools—so you can build, test, and debug workflows in your IDE just like any other Python code. Prefect introduced advanced features such as task mapping and dynamic task creation early on and has continued evolving toward event-driven patterns and lower runtime overhead in its more recent releases. At its core, Prefect handles the operational heavy lifting teams normally build: automatic state management and tracking, retries and failure handling, caching to avoid re-computation, and resume-from-last-success behavior so interrupted runs don’t need to start over. The runtime is designed for real-world dynamism—flows can branch, loop, and spawn tasks at runtime based on data or external signals, instead of requiring a pre-defined static graph. Prefect also provides a materialization/asset model for tracking important outputs, built-in logging and observability, and interactive flow capabilities (pauses, approvals, and human-in-the-loop steps) which are useful for complex data and ML workflows. Deployment and observability are first-class concerns. Developers can run and iterate locally with a single command, then deploy the same Python code to any execution environment: local processes, containers, Kubernetes, serverless platforms, cloud VMs, or managed Prefect infrastructure. Prefect separates orchestration from execution so your code runs where you choose while coordination and metadata live centrally. Teams can choose a self-hosted Prefect server for full control or use Prefect Cloud for a managed experience; the platform’s hybrid model requires only an outbound connection from execution environments, helping maintain privacy for code and data. Prefect also supports infrastructure-as-code patterns—examples include Helm and Terraform integrations—and provides tools for packaging per-flow environments so different jobs can use different dependencies and compute profiles without conflict. Prefect integrates with the ecosystem tools data teams use daily. There are ready-made integration packages for systems like dbt, event sources such as Debezium for change-data-capture tutorials, and client libraries (e.g., prefect-client) for lighter-weight API interactions. Typical use cases include ETL/ELT pipelines, orchestrating dbt projects, web scraping jobs with retries and logging, machine learning pipelines (training, evaluation, deployments), and real-time/CDC-driven automations. Organizations including Cash App, Rent the Runway, Endpoint, and dbt Labs are highlighted users that moved from legacy orchestration tools to Prefect to gain flexibility, better developer ergonomics, and improved observability. The open-source project (Apache 2.0 license) is backed by an active community, Slack, GitHub, and docs, and can be extended with custom blocks, plugins, and integrations to match bespoke platform needs. For teams building modern data platforms, Prefect offers a pragmatic balance of developer productivity and production reliability: write normal Python, get automatic retries, stateful resumes, and a visual dashboard for monitoring and debugging. Whether you need a self-hosted orchestrator for sensitive workloads or a managed cloud service for faster time-to-value, Prefect’s modular architecture, dynamic execution model, and broad integration surface make it a practical choice for ETL, ML, and event-driven workflows across organizations of all sizes.