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Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.
Enso Analytics is a self-service data prep and analysis platform designed for data teams.
Data processing for and with foundation models! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷
The open source repository for Electricity Maps data parsers that powers the world's most comprehensive electricity data platform
A Dockerized python Script to fetch Garmin health data and populate that in a InfluxDB Database, for visualization long term health trends with Grafana
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
visualized crawler & ETL IDE written with C#/WPF
Apache DevLake is an open-source dev data platform to ingest, analyze, and visualize the fragmented data from DevOps tools, extracting insights for engineering excellence, developer experience, and community growth.
Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.
Open-source ETL/ELT you deploy on your own servers or cloud. Built on DuckDB: no-code/low-code visual pipelines or SQL, 426 components, dbt, CDC, data quality, reverse ETL, lineage, MCP for AI agents. No vendor cloud, no per-row billing.
visual data prep powered by python
Dex : The Data Explorer -- A data visualization tool written in Java/Groovy/JavaFX capable of powerful ETL and publishing web visualizations.
📈 Collect customer event data from your apps. (Note that this project only includes the API collector, not the visualization platform)
A data migration and visualization command line gem in Ruby
Event-driven OneLake pipeline with automated Upsert merge and Python reporting
This is a library that provides generic functionalities for the implementation of imports. In addition to maximum performance and optimized memory consumption, Pacemaker can also be used to implement imports in distributed scenarios that place the highest demands on speed and stability.