Ideas
Dev toolsdata-processingalgorithmstreaming

Sliding window aggregation library

Build a lightweight library for real-time data aggregation using the two-stack sliding window algorithm. Target developers working with streaming data.

Why now

Real-time data processing is growing with IoT and edge computing, but efficient sliding-window implementations are still niche.

Who for
Data engineers, IoT developers
Business model
Commercial licensing
Effort
A few weeks

Processing streaming data efficiently requires specialized algorithms. The two-stack sliding window approach offers O(1) average time complexity for aggregations like min/max/sum.

Package this as a MIT-licensed library with bindings for Python, JavaScript, and Rust. Focus on clean APIs for common aggregation patterns.

Sell commercial licenses to enterprises using it at scale, while offering free tiers for open-source projects.

Start with a single-language implementation (Python first) demonstrating core operations, then expand.

The main risk is competition from existing time-series databases that bundle similar functionality.

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Sliding window aggregation library — Ideas