Open Source Data Management Systems - Page 14
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Recommenders
Best practices on recommendation systems
The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests. -
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Redisson
Valkey & Redis Java client. Real-Time Data Platform
Redisson is a Java client library for Redis that offers distributed data structures, services, and frameworks to build scalable and reliable applications. It simplifies Redis usage by providing in-memory Java objects like maps, sets, locks, queues, and semaphores that are backed by Redis. Redisson supports advanced features like distributed locking, asynchronous APIs, and integrates with frameworks like Spring and Quarkus for reactive and cloud-native development. -
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Reduce.jl
Symbolic parser for Julia language term rewriting using REDUCE algebra
REDUCE is a portable general-purpose computer algebra system. It is a system for doing scalar, vector and matrix algebra by computer, which also supports arbitrary precision numerical approximation and interfaces to gnuplot to provide graphics. It can be used interactively for simple calculations (as illustrated in the screenshot below) but also provides a full programming language, with a syntax similar to other modern programming languages. REDUCE supports alternative user interfaces including Run-REDUCE, TeXmacs and GNU Emacs. REDUCE (and its complete source code) is available free of charge for most common computing systems, in some cases in more than one version for the same machine. The manual and other support documents and tutorials are also included in the distributions. -
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Remotery
Single C file, Realtime CPU/GPU Profiler with Remote Web Viewer
Remotery is a real-time CPU/GPU profiler implemented as a single C file, providing developers with immediate insights into the performance of their applications. It features a remote web-based viewer that runs in browsers like Chrome, Firefox, and Safari, allowing for cross-platform performance analysis. Remotery supports profiling multiple threads and GPU contexts, offering a comprehensive view of an application's performance characteristics. -
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Searchkick
Intelligent search made easy
Searchkick brings powerful, production-ready search to Rails by mapping Active Record models into Elasticsearch with sensible defaults and easy customization. It supports language analyzers, stemming, synonyms, misspelling tolerance, and highlighting so search results feel natural to end users. Indexing is model-centric: you declare what fields to index, add computed fields, and trigger reindexing via callbacks or background jobs, with options for zero-downtime rolling reindexes. On the query side, a simple API covers relevance tuning, boosting, filtering, faceting/aggregations, and pagination, while still allowing direct access to advanced Elasticsearch features when needed. It integrates with Rails scopes and authorization patterns, making it straightforward to return only records the user can see. By wrapping complex search infrastructure in a clean Ruby interface, Searchkick lets teams deliver fast, relevant search experiences without becoming experts. -
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Self-learning-Computer-Science
Resources to learn computer science in your spare time
Self-learning Computer Science is a curated, open-source guide repository designed to help learners independently study computer science topics using high-quality university-level resources. The author (an undergraduate CS student) assembled links to courses from institutions like MIT, UC Berkeley, Stanford, etc., covering mathematics, programming, data structures/algorithms, computer architecture, machine learning, software engineering and more. It’s aimed at learners who find traditional course structures restrictive and want a flexible, self-paced path through CS, with a focus on building depth and breadth rather than shortcut exam skills. The repository provides a roadmap, references, teaching materials, and sometimes the author’s own project examples, offering both guidance and community support. Because the CS field is broad, the structure helps learners allocate study time, avoid duplication, and benefit from “best in class” resources instead of randomly browsing. -
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Semantic Type Detection
Metadata/data identification Java library
Metadata/data identification Java library. Identifies Base Type (e.g. Boolean, Double, Long, String, LocalDate, LocalTime, ...) and Semantic Type information (e.g. Gender, Age, Color, Country, ...). Extensive country/language support. Extensible via user-defined plugins. Comprehensive Profiling support. Large set of built-in Semantic Types (extensible via JSON defined plugins). Extensive Profiling metrics (e.g. Min, Max, Distinct, signatures, …) Sufficiently fast to be used inline. See Speed notes below. Minimal false positives for Semantic type detection. See Performance notes below. Usable in either Streaming, Bulk or Record mode. Broad country/language support - including US, Canada, Mexico, Brazil, UK, Australia, much of Europe, Japan and China. Support for sharded analysis (i.e. Analysis results can be merged) Once stream is profiled then subsequent samples can be validated and/or new samples can be generated. -
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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. -
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SuiteSparseGraphBLAS.jl
Sparse, General Linear Algebra for Graphs
A fast, general sparse linear algebra and graph computation package, based on SuiteSparse:GraphBLAS. -
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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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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing. Libraries from Python, R, C/Fortran, C++, and Java can also be used. -
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VoronoiFVM.jl
Solution of nonlinear multiphysics partial differential equations
Solver for coupled nonlinear partial differential equations (elliptic-parabolic conservation laws) based on the Voronoi finite volume method. It uses automatic differentiation via ForwardDiff.jl and DiffResults.jl to evaluate user functions along with their jacobians and calculate derivatives of solutions with respect to their parameters. -
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XLSX.jl
Excel file reader and writer for the Julia language
XLSX.jl is a Julia package to read and write Excel spreadsheet files. Internally, an Excel XLSX file is just a Zip file with a set of XML files inside. The formats for these XML files are described in the Standard ECMA-376. This package follows the EMCA-376 to parse and generate XLSX files. -
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YALMIP
MATLAB toolbox for optimization modeling
MATLAB toolbox for optimization modeling. -
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billboard.js
Re-usable, easy interface JavaScript chart library based on D3.js
billboard.js is a re-usable, easy interface JavaScript chart library, based on D3.js. The name "billboard" comes from the famous "billboard chart" which everybody knows. billboard.js provides the easiest way to create a 'chart' instantly. Chart generation is super easy. With extensive options, you can create a chart instantly! Yes, billboard.js works on D3 v4+; what everybody was waiting for! All of the code was written as an ESM(ES Module) with ES6+ syntax. Combine hundreds of options to satisfy your needs. Checkout the examples to see in action! Play with the diverse options generated on the fly! If you want to use 'billboard.js' without installation, load files directly from one of the CDN providers. -
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data-diff
Efficiently diff rows across two different databases
We're excited to announce the launch of a new open-source product, data-diff that makes comparing datasets across databases fast at any scale. data-diff automates data quality checks for data replication and migration. In modern data platforms, data is constantly moving between systems, and at the modern data volume and complexity, systems go out of sync all the time. Until now, there has not been any tooling to ensure that when the data is correctly copied. Replicating data at scale, across hundreds of tables, with low latency and at a reasonable infrastructure cost is a hard problem, and most data teams we’ve talked to, have faced data quality issues in their replication processes. The hard truth is that the quality of the replication is the quality of the data. Since copying entire datasets in batch is often infeasible at the modern data scale, businesses rely on the Change Data Capture (CDC) approach of replicating data using a continuous stream of updates. -
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errsole.js
Collect, Store, and Visualize Logs with a Single Module
Errsole is an open-source logger for Node.js. It has a built-in web dashboard to view, filter, and search your app logs. -
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forecast
Forecasting Functions for Time Series and Linear Models
The forecast package is a comprehensive R package for time series analysis and forecasting. It provides functions for building, assessing, and using univariate forecasting models (e.g. ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. Exponential smoothing state space models (ETS) including seasonal components. Residual checks, model accuracy, plots, forecast error measures etc. -
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ggplot2-book
ggplot2: elegant graphics for data analysis
The ggplot2-book repository contains the source for ggplot2: Elegant Graphics for Data Analysis, written by Hadley Wickham. The book explains the theory and application of the ggplot2 package in R for data visualization, guiding readers through its layered grammar of graphics approach. It serves as both a practical guide and conceptual reference for building advanced plots and mastering ggplot2. -
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gt R
Easily generate information-rich, publication-quality tables from R
With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer. It all begins with table data (be it a tibble or a data frame). You then decide how to compose your gt table with the elements and formatting you need for the task at hand. Finally, the table is rendered by printing it at the console, including it in an R Markdown document, or exporting it to a file using gtsave(). Currently, gt supports the HTML, LaTeX, and RTF output formats. -
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gusty
Making DAG construction easier
gusty allows you to control your Airflow DAGs, Task Groups, and Tasks with greater ease. gusty manages collections of tasks, represented as any number of YAML, Python, SQL, Jupyter Notebook, or R Markdown files. A directory of task files is instantly rendered into a DAG by passing a file path to gusty's create_dag function. gusty also manages dependencies (within one DAG) and external dependencies (dependencies on tasks in other DAGs) for each task file you define. All you have to do is provide a list of dependencies or external_dependencies inside of a task file, and gusty will automatically set each task's dependencies and create external task sensors for any external dependencies listed. gusty works with both Airflow 1.x and Airflow 2.x, and has even more features, all of which aim to make the creation, management, and iteration of DAGs more fluid, so that you can intuitively design your DAG and build your tasks. -
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hrbrthemes
Opinionated, typographic-centric ggplot2 themes and theme components
hrbrthemes is a focused ggplot2 theme package with an emphasis on typography, layout precision, and visual polish. It includes themes like theme_ipsum and Font scales tailored for clean, high‑quality production graphics. -
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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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luma.gl
High-performance Toolkit for WebGL-based data visualization
luma.gl is a GPU toolkit for the Web-focused primarily on data visualization use cases. luma.gl aims to provide support for GPU programmers that need to work directly with shaders and want a low abstraction API that remains conceptually close to the WebGPU and WebGL APIs. Unlike other common WebGL APIs, the developer can choose to use the parts of luma.gl that support their use case and leave the others behind. While generic enough to be used for general 3D rendering, luma.gl's mandate is primarily to support GPU needs of data visualization frameworks in the vis.gl suite. -
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md2googleslides
Generate Google Slides from markdown
Generate Google Slides from markdown & HTML. Run from the command line or embed in another application. This project was developed as an example of how to use the Slides API. While it does not yet produce stunningly beautiful decks, you are encouraged to use this tool for quickly prototyping presentations. The first time the command is run you will be prompted for authorization. OAuth token credentials are stored locally in a file named ~/.md2googleslides/credentials.json. Each time you will run the above comment, new slide deck will be generated. In order to work on exactly the same deck, just get the ID of the already generated slides. md2googleslides does not edit or control any theme-related options. Just set a base theme you want on Google Slides directly. Even if you will use, append option for deck reuse, theme will be not changed. Images can be placed on slides using image tags. Multiple images can be included.