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MongoDB Atlas runs apps anywhere
Deploy in 115+ regions with the modern database for every enterprise.
MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
A lightweight opinionated ETL framework, halfway between plain scripts
This package contains a lightweight data transformation framework with a focus on transparency and complexity reduction. Data integration pipelines as code: pipelines, tasks and commands are created using declarative Python code. PostgreSQL as a data processing engine. Extensive web ui. The web browser as the main tool for inspecting, running and debugging pipelines. GNU make semantics. Nodes depend on the completion of upstream nodes. No data dependencies or data flows. No in-app data...
This repository provides a Docker-powered CI tools demo environment via a single command with docker-compose. It assembles popular CI/CD components—Jenkins, SonarQube, Nexus, GitLab, and Selenium Grid—each running in separate containers, facilitating self-contained integration testing or workshops. It’s not intended for production but serves as a practical demo or launchpad for containerized CI stacks. Each tool runs in an isolated container for modular experimentation. Maintained primarily...
KETL(tm) is a production ready ETL platform. The engine is built upon an open, multi-threaded, XML-based architecture. KETL's is designed to assist in the development and deployment of data integration efforts which require ETL and scheduling
Framework for text mining, data integration and data analysis. Keywords: ontology and graph alignment, relation mining, warehouse, semantic database integration, bioinformatics, systems biology, microarray, Java.
DataSync Suite is an open source platform for integrating tools like Zimbra, SugarCRM, and Drupal. The tool is focused on a single sign-on, application data integration, and fast, flexible deployment.
The Stem Cell Artificial Neural network project entails the analysis and integration of genomics data for extracting the stemness signature of several tissues by training a multiclass single-layer linear artificial neural network.