I Built an Open-Source Firebase Analytics Alternative Because I Hit 1M Events/Day Once Too Many

📰 Hackernoon

Learn how to build an open-source alternative to Firebase Analytics to avoid event drop-offs at scale

advanced Published 19 Jun 2026
Action Steps
  1. Identify the limitations of hosted analytics solutions like Firebase Analytics
  2. Design a self-hosted analytics pipeline using NATS, Parquet, and BigQuery external tables
  3. Configure object storage to land raw events as Parquet files
  4. Implement a scalable analytics solution using open-source tools
  5. Test and validate the alternative analytics pipeline
Who Needs to Know This

Data engineers and product managers can benefit from this article to understand the limitations of hosted analytics and build a scalable solution

Key Insight

💡 Hosted analytics solutions can fail at scale, and self-hosted alternatives can provide more control and flexibility

Share This
🚀 Ditch Firebase Analytics event limits with Rawbbit, an open-source alternative! 📈

Key Takeaways

Learn how to build an open-source alternative to Firebase Analytics to avoid event drop-offs at scale

Full Article

A few years ago I was the data engineer on a mobile game soft launch when Firebase Analytics quietly started dropping events past its 1M/day cap. We didn't catch it for days. That experience pushed me to build Rawbbit — an open-source, Apache 2.0, self-hosted analytics pipeline that lands raw events as Parquet in your own object storage. This is the story of why hosted analytics fails at scale, why I chose NATS + Parquet + BigQuery external tables, and what I deliberately left out.
Read full article → ← Back to Reads

Related Videos

This could be the most perfect data frontend
This could be the most perfect data frontend
Matt Williams
How to Scrape Facebook Ad Library Data + Analyse on n8n 🔥
How to Scrape Facebook Ad Library Data + Analyse on n8n 🔥
DroidCrunch
6-Phase SQL Roadmap 2026 | Data Analytics & Engineering | #shorts
6-Phase SQL Roadmap 2026 | Data Analytics & Engineering | #shorts
SCALER
Label and One-Hot Encoding #ai #machinelearning #datascience #datacleaning #preprocessing
Label and One-Hot Encoding #ai #machinelearning #datascience #datacleaning #preprocessing
Ascent
How The Super Bowl Uses Machine Learning 🏈 #ai #nfl #superbowl #nextgen #machinelearning
How The Super Bowl Uses Machine Learning 🏈 #ai #nfl #superbowl #nextgen #machinelearning
Ascent
Modified Distribution Method (MODI) In Transportation Problem /Operations Research/Statistics
Modified Distribution Method (MODI) In Transportation Problem /Operations Research/Statistics
EZIKAN ACADEMY