analytics-pipeline

Track user events with Redis counters and flush aggregates to PostgreSQL.

783|62|Updated Jan 19, 2026

Search Tags:#analytics #postgresql #redis #real-time #data processing #event tracking

Ready to run

npx skills add https://github.com/dadbodgeoff/drift --skill analytics-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analytics-pipeline
Source: https://github.com/dadbodgeoff/drift/tree/main/drift%20v1%20depreciated/skills/analytics-pipeline
Command: npx skills add https://github.com/dadbodgeoff/drift --skill analytics-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of high-throughput event tracking by providing a system that can handle thousands of events per second without overwhelming a traditional database.

Core Features & Use Cases

High-Performance Event Tracking: Utilizes Redis counters for rapid ingestion of events.
Real-time Aggregation: Offers immediate access to current counts and hourly breakdowns.
Durable Storage: Periodically flushes aggregated data to PostgreSQL for long-term persistence and complex querying.
Use Case: Track user sign-ups, feature usage, or page views in real-time for a rapidly growing application, ensuring no data is lost due to write contention.

Quick Start

Use the analytics-pipeline skill to track a user signup event for user_abc.

Frequently Asked Questions about analytics-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I track high-throughput events in real-time without bottlenecking my PostgreSQL database?▼

To track high-throughput events without database bottlenecks, use Redis counters for rapid in-memory ingestion and periodically flush the aggregated data to PostgreSQL for durable persistence.

How does a Redis and PostgreSQL pipeline handle real-time event tracking?▼

A Redis and PostgreSQL pipeline handles real-time event tracking by using Redis counters for fast ingestion and immediate aggregation, then flushing the counts to PostgreSQL for long-term time-series storage.

What is the best way to store high-volume user activity data for both real-time and historical analysis?▼

The best way to store high-volume user activity data is using Redis to provide immediate access to real-time counts, while periodically flushing aggregated data to PostgreSQL for complex historical querying.

Can I use this analytics pipeline to track feature usage and page views at high throughput?▼

Yes, you can track feature usage and page views at high throughput because the pipeline ingests thousands of events per second via Redis counters, preventing write contention in your database.

Do I need both Redis and PostgreSQL to run this real-time data processing pipeline?▼

Yes, you need both Redis and PostgreSQL because Redis is required for fast in-memory counting during event ingestion and PostgreSQL is required for durable time-series data storage.

Why does writing user signups directly to a database cause write contention during traffic spikes?▼

Writing user signups directly to a database causes write contention because traditional databases cannot handle thousands of events per second, a problem solved by offloading ingestion to Redis counters.