hastic-zzz / hastic-server
Hastic data management server for analyzing patterns and anomalies from Grafana
self-hosted
docker
selfhosted
monitoring
metrics
prometheus
monitor
alerting
monitoring-server
monitoring-tool
analytics
anomaly-detection
elasticsearch
grafana
graphite
hastic-server
influxdb
pattern-detection
pattern-recognition
timeseries
Hastic-Server
Hastic-Server is an open-source time series analysis, anomaly detection, and pattern recognition server that integrates with Grafana for analyzing metrics, alerts, and events. It provides a REST API for managing time series data and detecting anomalies.
Features
- Time Series Data Management: Manage time series data from various sources including Graphite, InfluxDB, Prometheus, and Elasticsearch.
- Anomaly Detection: Detect anomalies in time series data using advanced statistical and machine learning algorithms.
- Pattern Recognition: Identify recurring patterns in time series data, such as seasonality and periodic events.
- Grafana Integration: Integrate with Grafana to visualize time series data, anomalies, and patterns.
- REST API: Provides a comprehensive REST API for data management, anomaly detection, and pattern recognition.
- Docker Support: Easy deployment using Docker containers.
- Self-Hosted: Can be self-hosted on your own infrastructure.
Use Cases
- Anomaly Detection: Monitor time series metrics and detect anomalies in real-time.
- Pattern Recognition: Identify trends, seasonality, and other patterns in time series data.
- Time Series Analysis: Perform advanced analysis on time series data, such as interpolation, aggregation, and forecasting.
- Grafana Visualization: Visualize time series data, anomalies, and patterns in Grafana.
- Customization: Extend and customize Hastic-Server using plugins and integrations.
Benefits
- Enhanced Monitoring: Improve the visibility and effectiveness of monitoring systems by detecting anomalies and identifying patterns.
- Predictive Analytics: Predict future behavior and identify potential risks or opportunities based on historical data patterns.
- Cost Optimization: Identify areas for performance improvement and resource optimization through pattern analysis.
- Open-Source and Extensible: Leverage a flexible and customizable platform to meet specific monitoring and analysis needs.