Eland

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Eland is an AI Agents & Automation tool that provides a Python client and toolkit for dataframes, big data, machine learning, and ETL in Elasticsearch. It offers a Pandas-compatible API for exploring and analyzing large datasets directly within Elasticsearch.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is eland?

Eland is a Python client and toolkit designed for seamless interaction with Elasticsearch, offering a Pandas-compatible API for data exploration and analysis. It enables users to work with large datasets residing in Elasticsearch without loading them into local memory, making it ideal for big data applications. Beyond data manipulation, Eland facilitates machine learning workflows by allowing users to upload trained regression and classification models from libraries like scikit-learn, XGBoost, and LightGBM directly into Elasticsearch for inference. It also supports importing PyTorch-trained BERT models for NLP tasks, including those from the Hugging Face model hub, enhancing Elasticsearch's capabilities for advanced AI applications and ETL processes.

Best used for

Ideal for data scientists and developers who need to analyze large datasets, deploy machine learning models, and perform ETL processes directly within Elasticsearch. Especially valuable for those working with big data and requiring a familiar Pandas-like interface for Elasticsearch interactions.

Common actions

analyze data

process big data

deploy machine learning models

perform ETL

integrate with Elasticsearch

github copilot"AI Agents"face swappingdeepfakeworkflowscollaborationopen-sourcelow-code/no-codeautomated workflow

Capabilities

Key features

Pandas-compatible API
Elasticsearch dataframes
ML model import
NLP with PyTorch
ETL processes
Big data analysis

Target Audience

data scientistdeveloper

Integrations

scikit-learnxgboostlightgbmpytorchhugging-face

Pricing & Plans

Open Source

Free

FAQs

What versions of Python, Pandas, and Elasticsearch does Eland support?

Eland supports Python 3.10, 3.11, 3.12, and 3.13. It is compatible with Pandas 1.5 and 2, and works with Elasticsearch 9+ clusters. For NLP features with PyTorch, the Eland minor version should match the Elasticsearch cluster's minor version.

Can Eland be used to upload custom machine learning models to Elasticsearch?

Yes, Eland allows transforming and uploading trained regression and classification models from common Python libraries like scikit-learn, XGBoost, and LightGBM into Elasticsearch for inference. It also supports importing PyTorch-trained BERT models for NLP tasks.

How does Eland handle large datasets without overloading local memory?

Eland is designed to keep data in Elasticsearch rather than loading it into local memory. It provides a Pandas-compatible API that defers processing and filtering of data to Elasticsearch, enabling efficient analysis of large datasets directly within the cluster.

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