DataFlux: Streaming Data Processing Pipeline
Project Overview
Modern IoT, finance, and logistics operations generate massive flows of real-time data. DataFlux was built as an enterprise-grade solution to ingest high-frequency data streams, run real-time statistical & ML preprocessing steps, flag anomalies instantly, and display the metrics on a latency-optimized live dashboard.
Role & Execution
DataFlux is a real-time machine learning pipeline designed to process and analyze streaming as well as batch data efficiently. It enables automated exploratory data analysis, feature engineering, preprocessing, and anomaly detection within a unified system. The platform uses WebSocket-based dashboards to provide live insights and monitoring capabilities. Built with scalable backend architecture, it supports high-throughput data processing with low latency. This system accelerates data-to-insight workflows and improves overall ML pipeline efficiency for real-world applications.
Outcomes & Impact
- Ingests and processes over 5,000 data points per second with sub-10ms processing latency.
- Instantly alerts users of anomalies within 50ms of event ingestion.
- Significantly accelerated data collection to model training cycles.
Tech Stack Details
Asynchronous design handles multiple input streams concurrently without blocking performance.
Provides rich analytical functions and efficient mathematical operations for anomaly scoring.
Pushes live-streamed data points straight to frontend graphs without constant polling.
Key Features
- High-throughput, non-blocking ingestion layer using asynchronous FastAPI.
- Real-time preprocessors for standardization, outlier removal, and imputations.
- Live anomaly detection module deploying Scikit-Learn models on stream.
- WebSocket connections providing low-latency visualization of data flows.
- Automated batch serialization saving model-ready data for future training.