Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
- batch-processing
- kafka
- pathway
- python
47,133 repositories indexed
1–24 of 26 · Most stars
Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
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.
Turns Data and AI algorithms into production-ready web applications in no time.
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
An orchestration platform for the development, production, and observation of data assets.
Real-time monitor and web admin for Celery distributed task queue
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
Koalas: pandas API on Apache Spark
CLI task management & automation tool
A lightweight opinionated ETL framework, halfway between plain scripts and Apache Airflow
Python Stream Processing
Elyra extends JupyterLab with an AI centric approach.
:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
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.
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
Streaming reactive and dataflow graphs in Python
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
Declarative data quality engine. Define checks in YAML, run anywhere.
🎲 Dotflow turns an idea into flow! — Lightweight Python library for execution pipelines