Orchestrate everything, everywhere. The open-source control plane for workflows across data, infrastructure, business processes and AI agents. Declarative YAML, event-driven, any language, Git-versioned, scalable to millions of executions.
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Declarative Continuous Deployment for Kubernetes
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
Open-source data movement for ELT pipelines and AI agents — from APIs, databases & files to warehouses, lakes, and AI applications. Both self-hosted and Cloud.
☁️ Build multimodal AI applications with cloud-native stack
Turns Data and AI algorithms into production-ready web applications in no time.
Always know what to expect from your data.
PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement
httpx is a fast and multi-purpose HTTP toolkit that allows running multiple probes using the retryablehttp library.
🍴 Web applications made easy. Since 2011.
📚 Parameterize, execute, and analyze notebooks
Kubernetes-native platform to run massively parallel data/streaming jobs
Machine learning platform for Web developers
【A common used C++ & Python DAG framework】 一个通用的、无三方依赖的、跨平台的、收录于awesome-cpp的、基于流图的并行计算框架。欢迎star & fork & 交流
The fastest unified streaming SQL Engine in a single C++ binary. ⚡ Millisecond latency. 100+ GB/s throughput. Continuously compute stateful context from streams, logs, metrics, events, and CDC.
A lightweight stream processing library for Go
A lightweight opinionated ETL framework, halfway between plain scripts and Apache Airflow
Composable concurrency toolkit for Go
MLBox is a powerful Automated Machine Learning python library.
A full-stack DevOps on AWS framework
Easy pipelines for pandas DataFrames.
🏎️ Streams & Reactive Programming paradigm for Go: declarative and composable API for event-driven applications
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.