Streamline-DAQ: Scalable Data Acquisition and Monitoring System

Overview

Streamline-DAQ is a distributed system designed for real-time data acquisition, processing, and monitoring. It enables efficient ingestion and analysis of high-frequency data streams from multiple sources, making it ideal for applications in scientific experiments, IoT systems, and high-performance environments. This was created to expand my project demonstrates expertise in distributed systems, real-time data processing, and monitoring for scalable, fault-tolerant operations.

Integration with CERN Open Data

The project integrates ATLAS Top Tagging Open Data from OpenData CERN to demonstrate its capabilities in handling high-energy physics datasets. Features include:

• Real-time ingestion using Apache Kafka.
• Preprocessing and feature engineering for scientific analysis.
• Scalable storage with PostgreSQL and MongoDB.
• Monitoring of data ingestion rates and tagging accuracy in Grafana.

Features

• Real-Time Data Acquisition: Simulates high-frequency data sources using Apache Kafka for distributed message brokering.
• Processing Pipelines: Processes time-series data with Python libraries (Pandas, NumPy), ensuring high throughput and minimal latency.
• Monitoring Dashboard: Provides actionable insights through real-time visualization of performance metrics (e.g., data rates, errors) using Grafana.
• Scalable Storage: Implements structured and unstructured data storage using PostgreSQL and MongoDB.
• Fault-Tolerant Architecture: Includes error recovery and retry mechanisms for uninterrupted data flow.

Architecture

1. Data Sources: Simulate IoT devices, sensors, or scientific instruments generating high-frequency data streams.
2. Data Ingestion: Apache Kafka ensures reliable, distributed ingestion of time-series data.
3. Processing Layer: Python scripts process, clean, and transform data for downstream tasks.
4. Storage Layer: Stores processed data in PostgreSQL or MongoDB for querying and analysis.
5. Monitoring Layer: Visualizes system health and performance metrics in Grafana.

Use Cases

• Scientific Data Systems: Real-time acquisition and analysis of experimental data.
• IoT Monitoring: Managing and visualizing high-frequency sensor data from IoT devices.
• Operational Monitoring: Tracking system performance metrics in distributed environments.

Setup and Installation

Prerequisites

• Operating System: Linux/macOS/Windows
• Languages: Python 3.9+
• Tools: Apache Kafka, PostgreSQL, Grafana, Docker

Installation

1. Clone the repository:

   git clone https://github.com/danigallegdup/streamline-daq.git
   cd streamline-daq

2. Install Python dependencies:

   pip install -r requirements.txt

3. Start services with Docker:

   docker-compose up

4. Access the Grafana dashboard at http://localhost:3000 (default credentials: admin/admin).

---

Usage

1. Simulate Data Sources:

   • Run the data_generator.py script to simulate multiple high-frequency data streams:

     python scripts/data_generator.py

2. Monitor System Performance:

   • Access Grafana to view throughput, latency, and error rates.

3. Query Processed Data:

   • Use SQL to query the PostgreSQL database for analysis:

     SELECT * FROM processed_data WHERE error_rate > 0.05;

---

Key Technologies

• Python: For data processing and pipeline development (Pandas, NumPy).
• Apache Kafka: Distributed message brokering for reliable data ingestion.
• PostgreSQL & MongoDB: For structured and unstructured data storage.
• Grafana: For real-time monitoring and visualization.
• Docker: For containerized service management.

Project Highlights

• High Throughput: Handles over 10,000 events per second with low latency.
• Scalability: Supports dynamic scaling for increased data loads.
• Reliability: Fault-tolerant architecture ensures uninterrupted data flow.

Future Improvements

• Implement machine learning models for real-time anomaly detection.
• Extend monitoring dashboards with predictive analytics.
• Add integration with cloud storage solutions for long-term data archiving.

Contributing

Contributions are welcome! Please submit a pull request or open an issue for any suggestions or improvements.

License

This project is licensed under the MIT License - see the LICENSE file for details.