ankane/blazer

Business intelligence made simple

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Summary Information

Updated 14 minutes ago
Added to GitGenius on September 14th, 2026
Created on October 1st, 2014
Open Issues & Pull Requests: 32 (+0)
GitHub issues: Enabled
Number of forks: 501
Total Stargazers: 4,802 (+0)
Total Subscribers: 70 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 21.4 hours
Mean response time: 158.1 days
90th percentile: 685.4 days
Tracked items: 29

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Issue Activity (beta)

Open issues: 4
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 561 days
Stale 30+ days: 3
Stale 90+ days: 3

Recent activity

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

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Detailed Description

Blazer is a business intelligence tool that lets teams explore databases with SQL, create charts and dashboards, and share insights across an organization.

Blazer solves the problem of making data accessible to non-technical team members without requiring them to write SQL directly. It works by providing a web interface mounted into a Rails application where users can run parameterized queries against multiple database backends including PostgreSQL, MySQL, and Redshift. Queries support variables that let users run the same analysis with different values, and the tool includes built-in features for creating visualizations, building dashboards, setting up data quality checks with email alerts, and tracking all query activity for audit purposes.

Teams should adopt Blazer if they already run Rails applications and want to embed analytics capabilities without building a separate tool. It suits organizations that need to give business users direct access to data while maintaining security through read-only database accounts and authentication integration with existing systems. The tool works with your application's authentication layer rather than requiring its own user management, and it can be protected with basic auth, Devise, or custom authorization methods. For sensitive data, the README points to complementary tools for data masking and encrypted field searching.

The project maintains active development with regular updates addressing user-reported issues and feature requests. The codebase shows responsiveness to production use cases, with particular attention to operational concerns like check scheduling flexibility across different job queue systems and configuration options for alert frequency. Documentation covers integration patterns for common deployment scenarios including cron-based scheduling and Slack notifications.