rudderlabs/rudder-server

Privacy and Security focused Segment-alternative, in Golang and React

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Summary Information

Updated 30 minutes ago
Added to GitGenius on September 15th, 2026
Created on July 19th, 2019
Open Issues & Pull Requests: 51 (+0)
GitHub issues: Enabled
Number of forks: 67
Total Stargazers: 4,487 (+0)
Total Subscribers: 62 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 3.2 days
Mean response time: 11.6 days
90th percentile: 37.0 days
Tracked items: 26

Most active contributors

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How this project is maintained

95% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Three people close 77% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 21
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 338 days
Stale 30+ days: 20
Stale 90+ days: 12

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • Need more info (6)
  • Stale (2)
  • WIP (1)

Most active issues this week

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Detailed Description

Rudder-server is a customer data platform that collects event data from applications and websites, then routes it to data warehouses and business tools.

The tool solves the problem of fragmented customer data by providing a unified pipeline for collection and activation. It works by accepting events from SDKs and source integrations, applying transformations, and delivering the data to warehouses like Snowflake, BigQuery, and Redshift as well as downstream business tools. The platform emphasizes privacy and security as core design principles, positioning itself as an open-source alternative to commercial CDPs.

Teams building customer data infrastructure should consider this tool if they want to avoid vendor lock-in through an open-source deployment option and need flexibility in how data flows through their stack. It suits organizations that require both event streaming capabilities and cloud-to-warehouse ELT patterns, with particular value for those already invested in data warehouses as their source of truth. The README positions the tool as a Segment alternative, emphasizing developer-friendly SDKs and an extensive library of integrations.

The project maintains active test and build workflows with visible status indicators. Development activity shows consistent attention to code quality through automated testing infrastructure. The tool is distributed via Docker, indicating a focus on containerized deployment patterns that reflect modern infrastructure practices.