Platform Capabilities
Real-time data processing with modern architecture and scalable design
Designed to handle high-volume data streams efficiently
Platform Features
Modern architecture demonstrating best practices for real-time data pipelines
High-Throughput Ingestion
Multi-protocol data ingestion supporting Kafka and MQTT for diverse telemetry sources.
Real-Time Processing
Spark Structured Streaming pipelines with windowed aggregations and sub-second processing latency.
Data Quality Assurance
Automated schema validation, drift detection, and data completeness checks at every pipeline stage.
Anomaly Detection
Statistical and ML-based anomaly detection with configurable alerting and threshold management.
Flexible Storage
Hybrid storage with PostgreSQL for OLTP and Parquet for analytics, optimized for query patterns.
Modern REST APIs
FastAPI-powered REST endpoints with async processing, authentication, and comprehensive documentation.
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System Architecture
Modular pipeline architecture built on popular open-source technologies
Ingestion Layer
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Stream Processing
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Storage Layer
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API Layer
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Consumption
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Ingestion Layer
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Stream Processing
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Storage Layer
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API Layer
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Consumption
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Complete Tech Stack
Built on popular, well-documented open-source technologies
Apache Kafka
v3.6.0
High-throughput distributed message broker
Apache Spark
v3.5.0
Unified analytics engine for batch and stream processing
PostgreSQL
v16.x
Advanced open-source relational database
FastAPI
v0.109.0
Modern, fast web framework for building APIs
Python
v3.11
Primary programming language
Redis
v7.2
In-memory data structure store
Docker
v24.0+
Container platform for deployment
Kubernetes
v1.28
Container orchestration platform
Grafana
v10.2.0
Analytics & monitoring platform
Prometheus
v2.48.0
Metrics collection & alerting
GitHub Actions
vLatest
CI/CD automation platform
TypeScript
v5.3.3
Type-safe JavaScript superset
Real-World Use Cases
Explore how StreamForge handles common streaming data challenges
IoT Fleet Monitoring
Demonstrates real-time monitoring of industrial sensors with anomaly detection
Scenario
Designed to monitor temperature, vibration, and power consumption from IoT sensors across simulated facilities in real-time.
Challenges
- •High volume of time-series data from diverse sensor types
- •Need for sub-second anomaly detection
- •Unreliable network connectivity in factory floors
Solutions
- •MQTT protocol for lightweight sensor communication
- •Local edge processing with Kafka buffering
- •Statistical anomaly detection on rolling windows
- •Automatic alerting via webhook integrations
Results
- •Real-time anomaly detection on sensor streams
- •Reliable ingestion with retry mechanisms
- •Threshold-based alerts for sensor anomalies
Application Performance Monitoring
Demonstrates microservices observability with request tracking
Scenario
Designed for platforms with multiple microservices requiring comprehensive monitoring and observability.
Challenges
- •Distributed tracing across multiple services
- •Identifying performance bottlenecks in real-time
- •Correlating errors across service boundaries
Solutions
- •Structured logging with trace ID propagation
- •Windowed aggregations for latency percentiles
- •Automated error rate alerting by endpoint
- •Real-time dashboards for monitoring
Results
- •Faster incident detection with correlated logs
- •Latency percentile tracking across endpoints
- •Resource utilization insights for optimization
Infrastructure Health Monitoring
Demonstrates server health monitoring with trend-based alerting
Scenario
Engineered to monitor server health metrics, detect anomalies, and optimize resource allocation across containerized infrastructure.
Challenges
- •Multiple metric types with time-series storage
- •Detecting gradual degradation vs. sudden failures
- •Balancing alerting sensitivity to avoid fatigue
Solutions
- •Columnar Parquet storage for efficient queries
- •Statistical models for anomaly detection
- •Multi-threshold alerting with severity levels
- •Automated runbook execution for common issues
Results
- •Proactive alerting for degrading metrics
- •Trend-based anomaly detection on storage metrics
- •Webhook-based alert routing and notification
Performance Benchmarks
Performance metrics from demo environment showcasing platform capabilities
Real-Time Throughput
Events processed per second over time
Platform Specifications
| Metric | Value | Notes |
|---|---|---|
| Throughput | ~2.5K events/sec | Single worker, local environment |
| P95 API Latency | ~250ms | FastAPI async endpoints |
| Storage Engine | PostgreSQL + Parquet | Hybrid OLTP/OLAP storage |
| Deployment | Docker Compose | Single-command local setup |
| Compression | 3:1 ratio (LZ4) | Kafka message compression |
Benchmarked in local Docker environment with simulated telemetry data
Transform Your
Data Operations
StreamForge is a full-stack streaming data platform built as part of the NeuraBoat ecosystem
Part of the NeuraBoat Ecosystem
StreamForge is one of several interconnected projects in the NeuraBoat ecosystem, designed to work together for end-to-end data intelligence solutions.