elastic/eland
Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch
This project helps data scientists and analysts work with very large datasets stored in Elasticsearch as if they were standard Pandas DataFrames. You can perform data exploration, filtering, and aggregations on your Elasticsearch data directly, without loading it all into your local machine's memory. It also enables you to upload trained machine learning models (like scikit-learn, XGBoost, LightGBM) from Python into Elasticsearch for scalable inference.
694 stars. Used by 1 other package. Actively maintained with 2 commits in the last 30 days. Available on PyPI.
Use this if you need to analyze or perform machine learning with large datasets residing in Elasticsearch, but prefer the familiar Pandas or scikit-learn syntax in Python.
Not ideal if your data is not stored in Elasticsearch or if you require highly custom, low-level control over Elasticsearch queries that go beyond a DataFrame-like abstraction.
Stars
694
Forks
107
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 13, 2026
Commits (30d)
2
Dependencies
5
Reverse dependents
1
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