Impressionist VS Rack::Tracker

Compare Impressionist vs Rack::Tracker and see what are their differences.

Rack::Tracker

Tracking made easy: Don’t fool around with adding tracking and analytics partials to your app and concentrate on the things that matter. (by railslove)
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Impressionist Rack::Tracker
1 0
1,523 642
0.3% 0.5%
0.0 0.0
4 months ago 9 days ago
Ruby Ruby
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Impressionist

Posts with mentions or reviews of Impressionist. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Impressionist yet.
Tracking mentions began in Dec 2020.

Rack::Tracker

Posts with mentions or reviews of Rack::Tracker. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Rack::Tracker yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing Impressionist and Rack::Tracker you can also consider the following projects:

Ahoy - Simple, powerful, first-party analytics for Rails

Legato - Google Analytics Reporting API Client for Ruby

Analytical

Gabba - Simple way to send server-side notifications to Google Analytics

Staccato - Ruby library to perform server-side tracking into the official Google Analytics Measurement Protocol

The Chartable Ruby gem - A lightweight and database-level Ruby library to transform any Active Record query into analytics hash ready for use with any chart library.

RequestResponseStats - A Ruby gem which captures request response statistics such as cycle time, memory allocation, etc. for each request response cycle grouped in configurable granularity level. As this library makes use of TCP protocol, using DataDog or NewRelic RPM would be way faster because of UDP protocol.