Open Source Linux Data Analytics Tools - Page 2
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Data Analytics Tools for Linux
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npm-pdfreader
Parse text and tables from PDF files.
npm-pdfreader is a Node.js library for reading text and parsing tables from PDF files. It supports tabular data with automatic column detection and rule-based parsing, making it useful for extracting structured data from PDFs. -
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R packages (maintained by YJLEE)
R packages for PK/PD modeling , BE/BA, drug stability, ivivc, etc.
These R packages are developed for data analysis of PK/PD modeling & simulation, bioequivalence/bioavailability (BE/BA), drug stability, in-vitro and in-vivo correlation (ivivc), as well as therapeutic drug monitoring (TDM). -
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FlowViewer
FlowViewer is a web-based netflow data analysis tool.
FlowViewer provides a convenient web-based user interface to Mark Fullmer’s flow-tools suite and CMU's netflow data capture/analyzer, SiLK. The inclusion of the underlying SiLK tool set enables FlowViewer users to continue to use the tool with the newer IPFIX netflow data protocol, which includes support for IPv6 and Cisco's v9 and FNF netflow. FlowViewer has been developed for NASA’s Earth Sciences Data and Information System (ESDIS) networks, and credit goes to NASA for their usual outstanding support of innovation. The FlowViewer tools provide additional graphing and tracking features by utilizing open source software including Thomas Boutrell’s gd, Lincoln Stein's GD, Martien Verbruggen's GD::Graph, and Tobias Oetiker’s RRDtool. FlowViewer v4.6 fixes non-UTC local time environments for FlowViewer and FlowGrapher, and $no_devices_or_exporters FlowMonitor_Collector problem. **Note: SiLK must be v3.8.0 or later. Version 3.9 handles sFlow data. -
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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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Python for Data Analysis
Materials and IPython notebooks for "Python for Data Analysis"
Python for Data Analysis is the official companion repository for Python for Data Analysis, 3rd Edition by Wes McKinney. It contains the datasets, examples, and IPython notebooks used throughout the book. The repository helps readers practice Python data analysis concepts directly in Jupyter Notebook. Its chapters cover Python basics, NumPy, pandas, data loading, cleaning, wrangling, visualization, time series, modeling libraries, and full analysis examples. The project includes setup options using uv or Conda, with dependency files to reproduce the working environment. It is best suited for learners, analysts, and developers who want hands-on practice with the modern Python data stack. -
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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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Eventer
Rapid, unbiased, reproducible analysis of synaptic events
Eventer is a programme designed for the detection of spontaneous synaptic events measured by electrophysiology or imaging. The software combines deconvolution for detection, and variable length template matching approaches for screening out false positive events. Eventer also includes a machine learning-based approach allowing users to train a model to implement their ‘expert’ selection criteria across data sets without bias. Sharing models allows users to implement consistent analysis procedures. The software is coded in MATLAB, but has been compiled as standalone applications for Windows, Mac and Linux. Please visit the official Eventer website for more info https://eventerneuro.netlify.app/ While the paper is in preparation, please cite as; Winchester, G., Liu, S., Steele, O.G., Aziz, W. and Penn, A.C. (2020) Eventer. Software for the detection of spontaneous synaptic events measured by electrophysiology or imaging. http://doi.org/10.5281/zenodo.3991676 -
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CloudQuery
The open-source cloud asset inventory powered by SQL
CloudQuery extracts, transforms and loads your cloud assets into normalized PostgreSQL tables. CloudQuery enables you to assess, audit, and monitor the configurations of your cloud assets. Use standard SQL to find any asset based on any configuration or relation to other assets. Connect CloudQuery standard PostgreSQL database to your favorite BI/Visualization tool such as Grafana, QuickSight, etc. Codify your security & compliance rules with SQL as the query engine. Integrate CloudQuery with your current visualization, monitoring, and alerting such as Grafana. CloudQuery supports the TimescaleDB PostgreSQL extension, giving you full historical snapshots of your cloud asset inventory. Data analysis, security, auditing, and compliance. Leverage SQL to get visibility into your cloud infrastructure and SaaS applications. Build a cloud-asset inventory across any of our supported official or community providers. -
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IoTDB
Apache IoTDB
Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Due to its light-weight architecture, high performance and rich feature set together with its deep integration with Apache Hadoop, Spark and Flink, Apache IoTDB can meet the requirements of massive data storage, high-speed data ingestion and complex data analysis in the IoT industrial fields. In the scene of factories, there are tens of devices under LAN network. IoTDB can be installed on a local controller server in the factory to receive data from those devices. The local controller server (normal PC or workstation) with IoTDB can provide the ability to persist data and query data with SQL-like interface. In addition, with TsFile-Sync tool, TsFiles on the local controller can be transmitted to the data center equipped with IoTDB instance in the cloud. -
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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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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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Sweetviz
Visualize and compare datasets, target values and associations
Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. Output is a fully self-contained HTML application. The system is built around quickly visualizing target values and comparing datasets. Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Shows how a target value (e.g. "Survived" in the Titanic dataset) relates to other features. Sweetviz integrates associations for numerical (Pearson's correlation), categorical (uncertainty coefficient) and categorical-numerical (correlation ratio) datatypes seamlessly, to provide maximum information for all data types. Automatically detects numerical, categorical and text features, with optional manual overrides. min/max/range, quartiles, mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness. -
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hctsa
Highly comparative time-series analysis
hctsa is a Matlab software package for running highly comparative time-series analysis. It extracts thousands of time-series features from a collection of univariate time series and includes a range of tools for visualizing and analyzing the resulting time-series feature matrix. -
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litlyx
Analytics for developers, setup Analytics in 30 seconds
The easiest, developer-centric analytics tool. Litlyxis an open-source, self-hostable analytics solution for the modern framework. Litlyx offers a unique eyewear cleaning system that includes a special cleaning solution and reusable microfiber swabs. This system is designed to provide a more thorough and eco-friendly way to clean glasses, lenses, and screens. The brand emphasizes sustainability by reducing single-use plastics and promoting long-term use of their products. Their cleaning kit is compact, portable, and designed to be effective for everyday use, ensuring that users can maintain clear vision without the hassle of disposable wipes or sprays. -
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nuSolve
Geodetic VLBI data analysis software.
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Epidat
Statistical data analysis
Programa multiplataforma de libre distribución para el análisis estadístico y epidemiológico de datos. Free distribution cross-platform program for statistical and epidemiological analysis of data. Sitio web: http://www.sergas.es/Saude-publica/EPIDAT Souceforge: https://sourceforge.net/projects/epidat/ Wikipedia: https://es.wikipedia.org/wiki/Epidat -
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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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.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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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community, we-media, and online earning community, with a QQ group of more than 10,000 people and at least 10,000 subscribers. The number of Github Stars exceeds 60k, and it ranks in the top 100 of all Github organizations. The daily up of all its websites exceeds 4k, and the peak of Alexa ranking is 20k. Our core members are certified as CSDN blog experts and short-book programmers as excellent authors. We have established ApacheCN, a non-profit document, and tutorial translation project. -
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AUR Malware Check
Detection tools for the June 2026 atomic-lockfile AUR supply-chain
AUR Malware Check is a community repository for detecting exposure to the June 2026 atomic-lockfile supply-chain attack against the Arch User Repository. It collects scattered indicators, affected package lists, and detection scripts into one place for easier review and contribution. The project helps users compare installed AUR packages against known compromised package lists. It also includes checks for related package-manager cache artifacts and supports broader historical scans through pacman logs. The repository provides shell-based tooling, a Python 3.14+ implementation, consolidated indicators, source notes, and testable detection resources. It is useful for Arch users, maintainers, and incident responders who need a focused way to investigate possible local exposure. -
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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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Amazon Kinesis Flink Connectors
Contains various Apache Flink connectors to connect to AWS data
This library contains various Apache Flink connectors to connect to AWS data sources and sinks. This repository contains various Apache Flink connectors to connect to AWS Kinesis data sources and sinks. Flink maintain backwards compatibility for the Sink interface used by the Firehose Producer. This project is compatible with Flink 1.x, there is no guarantee it will support Flink 2.x should it release in the future. An Apache Flink application is a Java or Scala application that is created with the Apache Flink framework. You author and build your Apache Flink application locally. Applications primarily use either the DataStream API or the Table API. The other Apache Flink APIs are also available for you to use, but they are less commonly used in building streaming applications. The Apache Flink DataStream API programming model is based on two components. -
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BDS
Blockchain data parsing and persisting results
JD Cloud Blockchain Data Service (BDS) is a real-time data aggregating, analyzing, and visualization service for chain-like unstructured data from all kinds of 3rd party Blockchains. Splitter is the key module of Blockchain Data Service (BDS) and provides data analysis capability. Splitter is responsible for consuming blockchain data from message queue (kafka) and inserting data into persistent data storage services (relational database, data warehouse, etc.) for further processing. Before compiling and running BDS, you must install go's compilation environment locally. -
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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. -
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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.
