📊 Real-Time Analytics with TuskLang & Go

Introduction

Real-time analytics isn’t a luxury—it’s a necessity. TuskLang and Go let you build streaming dashboards, anomaly detectors, and predictive engines with config-driven power and Go’s raw speed.

Key Features

- Streaming data processing - Live dashboards - Anomaly detection - Predictive analytics - Event-driven analytics - Multi-source analytics

Example: Streaming Analytics Config

[stream]
source: @http("GET", "wss://data.example.com/stream")
process: @go("analytics.ProcessEvent")
metrics: @metrics("events_per_sec", 0)

Go: Streaming Processor Example

package analytics
import (
  "github.com/gorilla/websocket"
  "log"
)
func ProcessEvent(event []byte) {
  // Parse, analyze, store
}

Live Dashboards

- Use TuskLang config to define dashboard sources - Go serves data via HTTP/WebSocket

Anomaly Detection

[anomaly]
detector: @go("analytics.DetectAnomaly")
threshold: 0.95

Predictive Analytics

- Use Go ML libraries (golearn, gorgonia) - TuskLang config: predict: @go("ml.Predict")

Event-Driven Analytics

[event]
trigger: @go("analytics.OnEvent")
action: @go("analytics.HandleAction")

Multi-Source Analytics

- Combine multiple @http, @query, @file.read sources

Best Practices

- Use @metrics for all key stats - Cache results with @cache - Secure endpoints with @env.secure

Troubleshooting

- Monitor Go logs for dropped events - Use TuskLang’s @metrics for real-time health

Conclusion

TuskLang + Go = analytics that never sleep. Stream, analyze, and act in real time—no excuses.