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charlotte-ruby avatar

charlotte-ruby/impressionist

0
View on GitHub↗
1,532 stars·309 forks·Ruby·MIT·5 views

Impressionist

Rails Plugin that tracks impressions and page views

Features

  • Analytics and Tracking - Tracks page views and impressions for Rails applications.
  • Logging and Monitoring - Logs page and action impressions.

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Frequently asked questions

What does charlotte-ruby/impressionist do?

Rails Plugin that tracks impressions and page views

What are the main features of charlotte-ruby/impressionist?

The main features of charlotte-ruby/impressionist are: Analytics and Tracking, Logging and Monitoring.

What are some open-source alternatives to charlotte-ruby/impressionist?

Open-source alternatives to charlotte-ruby/impressionist include: ankane/ahoy — Ahoy is a first-party analytics framework for Ruby on Rails applications. It provides tools for capturing user… 0xnr/awesome-analytics — This project is a curated directory of analytics frameworks and software designed to help users discover tools for… maiot-io/zenml — ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data… invertase/react-native-firebase — react-native-firebase is a modular set of libraries that integrates Firebase cloud services into cross-platform mobile… codeaprendiz/learn-devops — This project is a comprehensive educational repository designed to teach DevOps practices through structured learning… blue-cardinal/ngx-google-analytics — Google Analytics library for Angular projects.

Open-source alternatives to Impressionist

Similar open-source projects, ranked by how many features they share with Impressionist.
  • ankane/ahoyankane avatar

    ankane/ahoy

    4,464View on GitHub↗

    Ahoy is a first-party analytics framework for Ruby on Rails applications. It provides tools for capturing user behaviors and page views, allowing developers to maintain full ownership of their analytics data. The system distinguishes itself through relational data analytics, which links visit identifiers directly to application models for deep business object attribution. It includes a privacy-preserving tracking tool that utilizes IP address masking and cookie-less visitor grouping to anonymize user data. The project covers a broad range of analytics capabilities, including custom event tra

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  • 0xnr/awesome-analytics0xnr avatar

    0xnr/awesome-analytics

    4,286View on GitHub↗

    This project is a curated directory of analytics frameworks and software designed to help users discover tools for measuring user behavior, SEO performance, and mobile application metrics. It serves as a comprehensive resource list organized by specific use cases and tracking requirements. The directory includes a categorized index focused on privacy-preserving analytics software that prioritizes data protection. It covers a wide range of domains, including product analytics for tracking user journeys, SEO performance monitoring for organic search visibility, and general web and mobile tracki

    View on GitHub↗4,286
  • invertase/react-native-firebaseinvertase avatar

    invertase/react-native-firebase

    12,291View on GitHub↗

    react-native-firebase is a modular set of libraries that integrates Firebase cloud services into cross-platform mobile applications. It serves as a native-SDK wrapper, mapping JavaScript method calls to native iOS and Android Firebase SDKs via the React Native bridge to provide a type-safe interface for mobile backend integration. The project enables connectivity to a wide array of cloud services, including user authentication and identity management, NoSQL cloud databases with real-time synchronization, and scalable cloud storage for media files. It also provides tools for sending push notif

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  • maiot-io/zenmlmaiot-io avatar

    maiot-io/zenml

    5,452View on GitHub↗

    ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

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  • See all 30 alternatives to Impressionist→