Open Source Python Data Management Systems for Linux

Dagster is an orchestration platform for the development, production, and observation of data assets. Dagster as a productivity platform: With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. Dagster as a robust orchestration engine: Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally. Dagster as a unified control plane: The ‘single plane of glass’ data teams love to use. Rein in the chaos and maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities.

Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting.

Elementary is an open-source data observability solution for data & analytics engineers. Monitor your dbt project and data in minutes, and be the first to know of data issues. Gain immediate visibility, detect data issues, send actionable alerts, and understand the impact and root cause. Generate a data observability report, host it or share with your team. Monitoring of data quality metrics, freshness, volume and schema changes, including anomaly detection. Elementary data monitors are configured and executed like native tests in dbt your project. Uploading and modeling of dbt artifacts, run and test results to tables as part of your runs. Get informative notifications on data issues, schema changes, models and tests failures. Inspect upstream and downstream dependencies to understand impact and root cause of data issues.

Released August 22, 2013 Lubuntu Blends: Biochemistry 13.04 (Raring) v5.44 Linux Kernel Image 3.8.0-29 Lubuntu Blends are pre-installed Wubi disk image remixes of Ubuntu and Debian Science meta packages, A custom boot loader allows installations to be copied and automatically booted from most external or USB flash drives. Once up and running, use earlier Lubuntu Remix README instructions here until documentation is updated. https://sourceforge.net/projects/portable-linux/files/ Installation 1. Download the Wubi installer http://releases.ubuntu.com/saucy/wubi.exe 2. Install any flavor of Ubuntu. 3. Swap out the root.disk with the ones provided here. Overview LAMP stack running on localhost (127.0.0.1). Scientific, productivity & media packages include R (Rattle Data Miner), GridEngine, Condor, cooperative computing tools, WINE, LibreOffice, Evolution, Clinica, Neuro Debian Desktop, PsycoPy, OpenVibe, 3DSlicer, Paraview, Openshot. Cheers, Gregory Remington

UPDATE: Chromium is no longer updated or maintained. The project is frozen. Chromium is a flexible framework for scalable real-time rendering on clusters of workstations, derived from the Stanford WireGL project code base.

Ubix stands for Universal Business Intelligence Computing System. Ubix Linux is an open-source, Debian-based Linux distribution geared towards data acquisition, transformation, analysis and presentation. Ubix Linux purpose is to offer a tiny but versatile datalab. Ubix Linux is easily accessible, resource-efficient and completely portable on a simple USB key. Ubix Linux is a perfect toolset for learning data analysis and artificial intelligence basics on small to medium datasets. You can find additional information, technical guidance, and user credentials on the project website https://ubix-linux.sourceforge.io/ or on the project subreddit https://reddit.com/r/UbixLinux.

This project delivers Linux support (USB driver and Tools) for a Digital Signal Processor (DSP) cards of the Signal Ranger series, see http://www.softdb.com/product_dsp.shtml for details) connected to a Linux host via USB.

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.

Flightdeck-UI is a project that utilizes the ideas from the design of aircraft controls and instruments for creating computer user interfaces, such as for monitoring.

GXSM -- Gnome X Scanning Microscopy: A multi-channel image and vector-probe data acquisition and visualization system designed for SPM techniques (STM,AFM..), but also SPA-LEED/LEED/LEEM data analysis. A plug-in interface allows any user add-on data-processing and special hardware and instrument support. Latest: NC-AFM and related explorative methods as SQDM can be configured. High-Speed external PAC-PLL hardware option with digital DSP link. Based on several hardware options it supports a commercially available DSP hardware and provided also Open Source Code for all the low level signal processing tasks and instrument controls in a most flexible and adaptable manner. All latest software is available via SVN only or Live Demo/Install CD: http://www.ventiotec.de/linux/GXSM-Linux.iso

Karesansui is an open-source virtualization management application made in Japan. It's smart graphical user interface lowers your management cost, and brings a total management/audit solution for both physical and virtual servers. Full featured RESTful interface allows customizing and integration with other management/billing systems.

PBS Cluster Viz is a project to display information useful to admins and users about a computing cluster managed by a PBS-compatible resource manager. Information includes load and job distribution. Interactive as well as static output is available.

Retrieve statistics from SNMP-aware devices, store them in a database and graphically display them for monitoring and notification purposes.

The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.

idyuts is \"I Dare You to Use This Shell\"; a pre-hibernate approach to replacing an ORM written with jython functors into a pure-Java language command pattern. The \"pipeline codegen artifacts\" are simple IoC templates, and trivial to adapt

A tool to visualize interactively huge amounts of eventdata. Uses an innovative hierarchical zoomlens scaling from quarters down to 50 ms.

Yet another GUI for Kernel-based Virtual Machines.

A tool for autonomous and virtual topical data integration using the focused web-harvesting method.