Open Source Linux Data Analytics Tools
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Data Analytics Tools for Linux
View 15 business solutionsBrowse free open source Data Analytics tools and projects for Linux below. Use the toggles on the left to filter open source Data Analytics tools by OS, license, language, programming language, and project status.
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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DuckDB
DuckDB is an in-process SQL OLAP Database Management System
DuckDB is a high-performance analytical database system. It is designed to be fast, reliable and easy to use. DuckDB provides a rich SQL dialect, with support far beyond basic SQL. DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs), and more. For more information on the goals of DuckDB, please refer to the Why DuckDB page on our website. Processing and storing tabular datasets, e.g. from CSV or Parquet files. Interactive data analysis, e.g. Joining & aggregate multiple large tables. Concurrent large changes, to multiple large tables, e.g. appending rows, adding/removing/updating columns. Large result set transfer to client. For development, DuckDB requires CMake, Python3 and a C++11 compliant compiler. Run make in the root directory to compile the sources. For development, use make debug to build a non-optimized debug version. -
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Ahoy
Simple, powerful, first-party analytics for Rails
Ahoy is a first-party analytics library built primarily for Ruby on Rails, designed to let applications track visits and events in a clean, integrated way rather than relying on third-party tooling. It stores data in your own database by default, which gives developers full control over what data is captured, how it's processed, and how it’s used, sidestepping privacy concerns of external analytics providers. The library supports Rails, JavaScript, and native apps, making it flexible across front-end/back-end and mobile contexts. Because it’s designed for developers who already own their data stack, Ahoy encourages self-hosted analytics workflows, custom reporting, and integration with existing database infrastructure. It includes features for tracking visits (sessions), events (actions), and user properties, so you can introspect user journeys and behaviour within your application domain. -
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mrtg
MRTG - Multi Router Traffic Grapher
MRTG is a free, open-source tool designed to monitor and measure the traffic load on network links. It generates HTML pages containing graphical representations (PNG images) of network traffic, providing visual insights into bandwidth usage over time. Originally developed to monitor router traffic, MRTG has evolved to graph various network devices and other metrics. -
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Bdash
Simple SQL Client for lightweight data analysis
Simple SQL Client for lightweight data analysis. You can share the result with gist. Supports MySQL, PostgreSQL (Amazon Redshift), SQLite3, Google BigQuery, Treasure Data, Amazon Athena. You can download and install from Web Site or Releases. -
MongoDB Atlas runs apps anywhereMongoDB 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.
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Dash
Build beautiful web-based analytic apps, no JavaScript required
Dash is a Python framework for building beautiful analytical web applications without any JavaScript. Built on top of Plotly.js, React and Flask, Dash easily achieves what an entire team of designers and engineers normally would. It ties modern UI controls and displays such as dropdown menus, sliders and graphs directly to your analytical Python code, and creates exceptional, interactive analytics apps. Dash apps are very lightweight, requiring only a limited number of lines of Python or R code; and every aesthetic element can be customized and rendered in the web. It’s also not just for dashboards. You have full control over the look and feel of your apps, so you can style them to look any way you want. -
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teslamate
A self-hosted data logger for your Tesla
TeslaMate is an open-source self-hosted data logger that collects and visualizes data from Tesla vehicles in real time. It provides detailed insights into driving, charging, efficiency, and battery health through intuitive dashboards powered by Grafana. TeslaMate is ideal for Tesla owners who want full control of their vehicle data, avoid cloud reliance, and access rich analytics for personal tracking or troubleshooting. -
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Blue Whale Configuration Platform
Blue Whale smart cloud configuration platform
Has accumulated experience in supporting hundreds of Tencent businesses, compatible with various complex system architectures, born in operation and maintenance, and proficient in operation and maintenance. From configuration management to job execution, task scheduling and monitoring self-healing, and then through operation and maintenance big data analysis to assist operational decision-making, it covers the full-cycle assurance management of business operations in a comprehensive manner. The open PaaS has a powerful development framework and scheduling engine, as well as a complete operation and maintenance development training system, which helps the rapid transformation and upgrading of operation and maintenance. Through the Blue Whale intelligent cloud system, it can help enterprises quickly realize the automation of basic operation and maintenance services, thereby accelerating the transformation of DevOps, realizing a tool culture, and maximizing operational efficiency. -
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Link-Preview-JS
Extract web links information: title, description, images, videos, etc
link-preview-js is a lightweight TypeScript library that extracts metadata from URLs or HTML content to generate rich link previews. By parsing Open Graph tags and other metadata, it retrieves information such as titles, descriptions, images, and videos. Designed primarily for Node.js and mobile environments, it facilitates the creation of link previews similar to those found on social media platforms. -
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PivotTable.js
Open-source Javascript Pivot Table
PivotTable.js is a powerful JS library (originally CoffeeScript) that brings Excel-style pivot tables to the browser. Built atop jQuery and jQueryUI, it allows users to drag and drop fields into rows, columns, and aggregators to slice and summarize datasets. It also integrates with Python and R via modules like pivottablejs, making it versatile for data analysis across platforms. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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qvge
Qt Visual Graph Editor
qvge is a multiplatform graph editor written in C++/Qt. Its main goal is to make possible visually edit two-dimensional graphs in a simple and intuitive way. Please note that qvge is not a replacement for such a software like Gephi, Graphvis, Dot, yEd, Dia and so on. It is neither a tool for "big data analysis" nor a math application. It is really just a simple graph editor :) -
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CT4AM
Tools for 3D image analysis of additively manufactured parts
CT4AM is a cross-platform toolkit for 3D-image processing in the domain of additive manufacturing for toolpath visualisation or correlation to tomographic data and related materials science. It is written in C++ and provides small, problem-specific applications for viewing, analysing and visualising of volumetric data from micro-CT scans with respect to the complex process parameters and characteristics of additive manufacturing. Moreover, data analysis and processing of G-code are key assets of the project. The main objective is not to provide yet another complex application for volume data visualization and medical image processing. Instead, CT4AM is a collection of small and simple apps that have already been successfully used to solve various problems in materials science and process visualization. You can either execute these tools directly or use their source codes to develop your own application for toolpath analysis or correlation. -
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Crystalsim - XRD hkl simulation
X-ray diffraction (XRD) analysis for hkl simulation of any crystal.
Crystalsim is a simple freeware program with a neat graphical user interface for X-ray diffraction (XRD) data analysis . It can simulates all possible {hkl} planes data for the selected crystal. Crystallographic Information File (.cif) can also be used. Analyze both powder diffraction and single crystal data . Indexed at International Union of Crystallography (IUCR). Crystalline lattice parameters such as ‘a’, ‘b’, ‘c’ as well as interfacial angles such as alpha, beta, gamma can also be entered manually. Processed data can be saved as .csv file format. Designed by M Kanagasabapathy, Assistant Professor, Department of Chemistry, Rajus' College, Affiliated to Madurai Kamaraj University Rajapalayam (TN) India email: rrcmks(at)gmail.com -
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PlateEditor
PlateEditor, a free web application to work with multi-well plates
PlateEditor is a free web application to work with multi-well plates, from creation of layouts to data visualization and aggregation. It is primarily designed as a tool to help scientists working in the field of biology to simplify and fasten the process of data analysis. It is fully client-side: files are never transferred to the server, ensuring complete security and privacy of research data. It doesn't require any installation. - PlateEditor is available at this address: https://plateeditor.sourceforge.io - The source code is also available on GitHub, visit: https://github.com/vindelorme/PlateEditor More information about the source code and the API are available there (the wiki is still under construction, thanks for your patience!) For a list of recent updates, visit: https://sourceforge.net/p/plateeditor/wiki/Updates/ Hoping PlateEditor will make your life easier in the lab! If yes, please cite us: https://doi.org/10.1371/journal.pone.0252488 -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
.NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write .NET code allowing you to reuse all the knowledge, skills, code, and libraries you already have as a .NET developer. .NET for Apache Spark runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. It also runs on all major cloud providers including Azure HDInsight Spark, Amazon EMR Spark, AWS & Azure Databricks. -
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123VCF
An Intuitive and Efficient Tool for VCF file filtration
123VCF has been developed to make the filtration step of VCF files efficient and more importantly easy to understand. It can be used in the most important step of whole exome/genome sequencing data analysis in the research and also clinical settings. User manual: https://dl.adbioinformatics.net/123VCF/123VCF_Manual.ver2.pdf If you use 123VCF, please cite its paper: Eidi, M., Abdolalizadeh, S., Moeini, S. et al. 123VCF: an intuitive and efficient tool for filtering VCF files. BMC Bioinformatics 25, 68 (2024). https://doi.org/10.1186/s12859-024-05661-5 _____________________________________ Authors: Milad Eidi, Samaneh Abdolalizadeh, Soheila Moeini Supervisors: Javad Zahiri, PhD - Masoud Garshasbi, PhD Department of Neuroscience, University of California San Diego, California, USA Department of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran -
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Cookiecutter Data Science
Project structure for doing and sharing data science work
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards, ultimately, data science code quality is about correctness and reproducibility. It's no secret that good analyses are often the result of very scattershot and serendipitous explorations. Tentative experiments and rapidly testing approaches that might not work out are all part of the process for getting to the good stuff, and there is no magic bullet to turn data exploration into a simple, linear progression. -
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JS Analyzer
Burp Suite extension for JavaScript static analysis
JS Analyzer is a powerful static analysis tool implemented as a Burp Suite extension that helps security researchers and web developers automatically uncover important artifacts in JavaScript files during web application testing. It parses JavaScript responses intercepted by Burp Suite and intelligently extracts API endpoints, full URLs (including cloud storage links), secrets like API keys or tokens, and email addresses while filtering out noise from irrelevant code patterns. The extension is designed to reduce manual effort when analyzing large or obfuscated JavaScript assets, helping testers find security vulnerabilities and sensitive information faster and more reliably. It also includes UI features such as live search, result filtering, and the ability to export findings in JSON format for further processing. The underlying engine can be used independently in Python, enabling integration into custom workflows or automated pipelines outside Burp Suite. -
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JSMN
Jsmn is a world fastest JSON parser/tokenizer
JSMN (pronounced "jasmine") is a minimalistic JSON parser in C. It's tailored for resource-constrained environments, making it ideal for embedded systems where efficiency and low memory footprint are paramount. -
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JavaParser
Java 1-17 Parser and Abstract Syntax Tree for Java
This project contains a set of libraries implementing a Java 1.0 - Java 17 Parser with advanced analysis functionalities. The project binaries are available in Maven Central. We strongly advise users to adopt Maven, Gradle or another build system for their projects. If you are not familiar with them we suggest taking a look at the maven quickstart projects. Since Version 3.5.10, the JavaParser project includes the JavaSymbolSolver. While JavaParser generates an Abstract Syntax Tree, JavaSymbolSolver analyzes that AST and is able to find the relation between an element and its declaration (e.g. for a variable name it could be a parameter of a method, providing information about its type, position in the AST, etc). When choosing open source technologies it is important to know your choice will be rewarded by continuous support. The JavaParser community is vibrant and active, with a weekly release cadence that supports language features up to Java 12. -
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Kapacitor
Open source framework for processing, monitoring, and alerting
Open source framework for processing, monitoring, and alerting on time series data. Kapacitor is a real-time data processing engine for monitoring and alerting, specifically designed to work with time-series data from InfluxDB. -
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MTTK Open BI
A user-friendly lightweight BI tool.
A user-friendly lightweight BI tool. It is a set of plugins of MTTK Lowcode Engine, so the user manual and develop manual are combined together with MTTK Low Code Engine. Code repository: https://github.com/jamie-mttk/mttk-lowcode-designer Online demo available at http://139.129.210.30:8825 (First visit may be slow) Username: admin Password: 123456 Lightweight No data processing engine offered, all the data processing depends on the target Databse Server. Easy to use For example ,to build an echart need to provide many many configurtaions/options, such as title location, grid position,etc. To simplified the usage ,only the important options should be configured by user with default setting, other options are set by BI system automatically. -
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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic). File sizes, creation dates, dimensions, indication of truncated images and existance of EXIF metadata. Mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint). Comprehensive and automatic list of potential data quality issues (high correlation, skewness, uniformity, zeros, missing values, constant values, between others). -
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Poli
An easy-to-use BI server built for SQL lovers. Power data analysis
An easy-to-use BI server built for SQL lovers. Power data analysis in SQL and gain faster business insights. Platform independent web application. Single JAR file + Single SQLite DB file. Get up and running in 5 minutes. PostgreSQL, Oracle, SQL Server, MySQL, Elasticsearch... You name it. No ETLs, no generated SQL, polish your own SQL query to transform data. Pixel-perfect positioning + Drag'n'Drop support to customize the reports and charts in your own way. Utilize the power of dynamic SQL with query variables to connect Filters and Charts. Capture the snapshot of historical data. Free up space in your own database. Three system level role configurations + Group based report access control. Custom the language pack and translations just for your audience. Auto refresh, drill through, fullscreen, embeds, color themes + more features in development. -
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SandDance
Visually explore, understand, and present your data
By using easy-to-understand views, SandDance helps you find insights about your data, which in turn help you tell stories supported by data, build cases based on evidence, test hypotheses, dig deeper into surface explanations, support decisions for purchases, or relate data into a wider, real world context. SandDance uses unit visualizations, which apply a one-to-one mapping between rows in your database and marks on the screen. Smooth animated transitions between views help you to maintain context as you interact with your data. This new version of SandDance has been rebuilt from scratch with the goal of being modular, extensible, and embeddable into your custom applications. We are open and driven by the community through contributions, feature requests, and discussion. SandDance was created by the Microsoft Research VIDA Group which explores novel technologies for visualization and immersive data analytics. -
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StarRocks
StarRocks is a next-gen sub-second MPP database for full analytics
StarRocks is the next generation of real-time SQL engines for enterprise analytics. Real-time analytics is notoriously difficult. Complex data pipelines and de-normalized tables have always been a necessary evil. Processing any updates or deletes once data arrives has not been possible- until now. StarRocks solves these challenges and makes real-time analytics easy. Get amazing query performance on Star or Snowflake Schemas directly. From canceled orders to updated items, your analytics applications can easily handle them with StarRocks. From streaming data to change data capture, StarRocks meets the data ingestion demands of real-time analytics. Scale storage and computing power horizontally and support tens of thousands of concurrent users. All of your BI tools work with StarRocks through standard SQL. StarRocks provides superior performance. It is also a unified OLAP covering most data analytics scenarios.
