Search Results for "source testing unit testing"
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Showing 136 open source projects for "source testing unit testing"
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Apache Airflow Provider
Great Expectations Airflow operator
Due to apply_default decorator removal, this version of the provider requires Airflow 2.1.0+. If your Airflow version is 2.1.0, and you want to install this provider version, first upgrade Airflow to at least version 2.1.0. Otherwise, your Airflow package version will be upgraded automatically, and you will have to manually run airflow upgrade db to complete the migration. This operator currently works with the Great Expectations V3 Batch Request API only. If you would like to use the... -
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MarketData.jl
Time series market data
The MarketData package provides open-source financial data for research and testing. The data is from Quandl and is free end-of-day stock data. It is public domain without restrictions. The TimeSeries TimeArray data structure is used to store the data, but conversion to other data structures, including DataFrames and AxisArrays, is supported. -
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Sylius
Open source eCommerce platform on Symfony
Sylius provides modern software and development practices that encode a customer-centric approach and agility in your team's processes and technology stack. This holistic strategy unleashes your full potential and makes sure that your business adapts to the ever-changing demands of local and global commerce markets. Sylius is an Open Source Headless eCommerce Platform for mid-market and enterprise brands that need custom solutions. With a modern approach to the professional development of... -
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CausalityTools.jl
Algorithms for detecting associations, dynamical influences
CausalityTools.jl is a package for quantifying associations and dynamical coupling between datasets, independence testing, and causal inference. Association measures from conventional statistics, information theory, and dynamical systems theory, for example, distance correlation, mutual information, transfer entropy, convergent cross mapping and a lot more. A dedicated API for independence testing, which comes with automatic compatibility with every measure-estimator combination you can... -
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. -
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XState
State machines and statecharts for the modern web
JavaScript and TypeScript finite state machines and statecharts for the modern web. Statecharts are a formalism for modeling stateful, reactive systems. This is useful for declaratively describing the behavior of your application, from the individual components to the overall application logic. XState is a library for creating, interpreting, and executing finite state machines and statecharts, as well as managing invocations of those machines as actors. The following fundamental computer... -
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Malli
High-performance data-driven data specification library
Malli is a powerful, data-driven schema library for Clojure and ClojureScript, offering rich support for specification, validation, parsing, error reporting, and generative testing. Designed for performance, Malli leverages efficient runtime representations and code generation, seamlessly integrating with Clojure’s data-oriented architecture. It supports function schemas, JSON transformation, and OpenAPI generation for strong API contracts. -
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ReTest.jl
Testing framework for Julia
ReTest is a testing framework for Julia allowing defining tests in source files, whose execution is deferred and triggered on demand. This is useful when one likes to have definitions of methods and corresponding tests close to each other. This is also useful for code that is not (yet) organized as a package, and where one doesn't want to maintain a separate set of files for tests. -
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Gradle Docker Compose Plugin
Simplifies usage of Docker Compose for integration testing
The Gradle Docker Compose Plugin by Avast integrates Docker Compose lifecycle management into Gradle builds. It allows developers to define and manage Docker containers required for integration testing or local development directly from their Gradle build scripts. This plugin automates the startup and shutdown of services, supports container health checks, and enables tight integration between application code and containerized services, enhancing reproducibility and automation in... -
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DQO Data Quality Operations Center
Data Quality Operations Center
DQO is an DataOps friendly data quality monitoring tool with customizable data quality checks and data quality dashboards. DQO comes with around 100 predefined data quality checks which helps you monitor the quality of your data. Table and column-level checks which allows writing your own SQL queries. Daily and monthly date partition testing. Data segmentation by up to 9 different data streams. Build-in scheduling. Calculation of data quality KPIs which can be displayed on multiple built-in... -
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ggstatsplot
Enhancing {ggplot2} plots with statistical analysis
{ggstatsplot} is an extension of {ggplot2} package for creating graphics with details from statistical tests included in the information-rich plots themselves. In a typical exploratory data analysis workflow, data visualization and statistical modeling are two different phases: visualization informs modeling, and modeling in its turn can suggest a different visualization method, and so on and so forth. Bayesian hypothesis-testing. The central idea of {ggstatsplot} is simple: combine these... -
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Cucumber
Cucumber for Ruby
It’s simple. Whether open source or commercial, our collaboration tools will boost your engineering team's performance by employing Behavior-Driven Development (BDD). And with our world-class training, take it to places it’s never been. Cucumber is a tool for running automated tests written in plain language. Because they're written in plain language, they can be read by anyone on your team. Because they can be read by anyone, you can use them to help improve communication, collaboration and... -
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Great Expectations
Always know what to expect from your data
Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. Expectations... -
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Conversational Health Agents (CHA)
A Personalized LLM-powered Agent Frameworks
...The framework supports modular components such as planning, tool execution, and multimodal input processing, which makes it suitable for complex healthcare applications. It also includes a web-based interface for interacting with the agent, making it accessible for testing and deployment in real-world scenarios. -
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Marketing Skills for Claude Code
Marketing skills for Claude Code and AI agents
Marketing Skills for Claude Code is an extensive library of structured “skills” designed to empower Claude Code and similar AI coding agents to perform specialized marketing tasks with strategic depth, helping technical marketers and founders apply best practices in areas such as conversion rate optimization, SEO, analytics, copywriting, and growth engineering directly in an AI-assisted workflow. Each skill in the repository is expressed as a Markdown file that encapsulates specific... -
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Scio
A Scala API for Apache Beam and Google Cloud Dataflow
Scio is a Scala API developed by Spotify that builds on Apache Beam to enable expressive batch and streaming data pipelines, optimized for running on Google Cloud Dataflow. Inspired by Spark and Scalding, it provides scalable, type‑safe, and production-grade data processing, with built-in support for BigQuery, Pub/Sub, Cassandra, Elasticsearch, Redis, TensorFlow IO, and more. -
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ODD Platform
First open-source data discovery and observability platform
Unlock the power of big data with OpenDataDiscovery Platform. Experience seamless end-to-end insights, powered by unprecedented observability and trust - from ingestion to production - while building your ideal tech stack! Democratize data and accelerate insights. Find data that fits your use case and discover hints left by your peers to leverage existing knowledge. Explore tags, ownership details, links to other sources and other information to shorten and simplify data discovery phase.... -
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The ARCHModels Package for Julia
A Julia package for estimating ARMA-GARCH models
ARCH (Autoregressive Conditional Heteroskedasticity) models are a class of models designed to capture a feature of financial returns data known as volatility clustering, i.e., the fact that large (in absolute value) returns tend to cluster together, such as during periods of financial turmoil, which then alternate with relatively calmer periods. This package provides efficient routines for simulating, estimating, and testing a variety of GARCH models. ARCH (Autoregressive Conditional... -
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RStudio
RStudio is an integrated development environment (IDE) for R
RStudio is a powerful, full-featured integrated development environment (IDE) tailored primarily for the R programming language but increasingly supportive of other languages like Python and Julia. It brings together console, editor, plotting, workspace, history, and file-management panes into a unified interface, helping data scientists, statisticians, and analysts to work more productively. The IDE is cross-platform: there are desktop versions for Windows, macOS and Linux, as well as a... -
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Pyper
Concurrent Python made simple
Pyper is a Python-native orchestration and scheduling framework designed for modern data workflows, machine learning pipelines, and any task that benefits from a lightweight DAG-based execution engine. Unlike heavier platforms like Airflow, Pyper aims to remain lean, modular, and developer-friendly, embracing Pythonic conventions and minimizing boilerplate. It focuses on local development ergonomics and seamless transition to production environments, making it ideal for small teams and... -
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gridstack.js
Build interactive dashboards in minutes
gridstack.js is a modern Typescript library designed to help developers create beautiful draggable, resizable, responsive layouts with just a few lines of code. You can create more advanced grids: allow users to persist changes, create a sidebar of widgets to drag into your grid, nested grids, and much more. Mobile-friendly modern Typescript library for dashboard layout and creation. Making a drag-and-drop, the multi-column responsive dashboard has never been easier. Has multiple bindings... -
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lakeFS
lakeFS - Git-like capabilities for your object storage
...Easier ETL testing - test your ETLs on top of production data, in isolation, without copying anything. Safely experiment and test on full production data. Easily Collaborate on production data with your team. Automate data quality checks within data pipelines. -
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Gumroad
Sell stuff and see what sticks
...The UI offers product pages, carts, and dashboards for creators to manage listings and see performance, while APIs/webhooks enable integrations with external sites and automation. The codebase demonstrates practical patterns for multi-tenant architecture, permissions, testing, and asynchronous processing at scale. For engineers, it’s both a working marketplace and a study in how to balance flexibility for creators with reliability in payments and delivery. -
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LinearSolve.jl
High-Performance Unified Interface for Linear Solvers in Julia
LinearSolve.jl is a unified interface for the linear solving packages of Julia. It interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. Performance is key: the current methods are made to be highly performant on scalar and statically sized small problems,... -
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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...
