beava-dev

beava-dev / beava

Public

Real-time feature server for fraud, ad-tech, and behavioral analytics.

analytics feature-store fraud-detection python real-time
13
0
100% credibility
Found May 07, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

Beava is a lightweight server that processes live events to deliver ultra-fast computed features for fraud detection, advertising, and user analytics via an easy Python setup.

How It Works

1
🔍 Discover Beava

You learn about Beava, a handy tool that instantly turns your live user actions into smart insights for catching fraud or understanding behavior.

2
📥 Start the server

You grab the ready-to-go software and launch it on your machine with a single easy step, no hassle.

3
✨ Sketch your insights

In a simple note-like script, you describe the actions to watch—like clicks or purchases—and the patterns to spot, like speeds or totals.

4
🔗 Link it up

Your script shares those descriptions with the running server, setting everything in motion.

5
📤 Feed in real actions

As things happen in real life, like user visits or buys, you send them right over.

6
⚡ Grab fresh insights

You ask for the latest patterns, and they appear super fast, helping you decide on the spot.

🎉 Insights at your fingertips

Now you have real-time smarts flowing effortlessly, powering alerts or custom experiences without any headaches.

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Star Growth

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AI-Generated Review

What is beava?

Beava is a Rust-built real-time feature server for fraud detection, ad-tech, and behavioral analytics. Push events via HTTP or TCP, define features using Python decorators for counters, velocities, distributions, and 50+ aggregations, then query per-entity state at sub-millisecond latency. It acts like Redis for streaming features but with atomic updates and no Lua scripting hassle—ideal for github real time data pipelines without brokers or ETL.

Why is it gaining traction?

It ditches the Postgres triggers + Redis counters + cron drift nightmare for a single binary with WAL durability and snapshots, handling 600k+ events/sec on consumer hardware. Python SDK quickstarts let you register schemas and push/query in 10 lines, with real time feature computation like windowed counts or geo-velocity out of the box. Sub-ms gets via TCP msgpack make it a real time api github standout over heavyweights like Flink.

Who should use this?

Fraud engineers scoring live transactions on velocity or entropy signals. Ad-tech devs tracking user behavior across sessions without Kafka overhead. Analytics teams building real time dashboards for ecommerce bursts or real time detection rules.

Verdict

Try it if you need real time feature extraction without infra sprawl—quickstart and benchmarks are solid. At 13 stars and 1.0% credibility, it's early alpha; test perf on your workload before prod.

(187 words)

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