The Power of Data
Data Platform Overview
Our Data Platform excels in real-time data visualization, offering powerful capabilities for monitoring and analyzing live data streams.
Key Features
Our core platform enables broadcasting real-time events and data to dashboards.
It allows for streaming data out of the box, pushing updates to all clients simultaneously, which is a significant improvement over the previous pull-based model
Data Streaming: Supports multiple methods for streaming real-time data:
- Using data source plugins (e.g., MQTT)
- Posting updates directly to the server over HTTP or WebSocket
- Integration with tools like Telegraf for pushing data to dashboards.
Real-Time Peformance
Our Data Platform excels in real-time data visualization, offering powerful capabilities for monitoring and analyzing live data streams. It supports various data sources and provides a rich set of visualization options, making it ideal for real-time performance monitoring.
Data Visualization
Our data platform offers a wide range of visualization types suitable for real-time data, including:
- Time series graphs
- Gauges
- Heat-maps
- Bar charts
- State timelines
- Many others…
Data Storage
Our Data Platform significantly improves data storage performance compared to previous versions. It offers 4.5x better storage compression, potentially reducing storage costs by over 90%. This is achieved through the use of object storage and advanced compression techniques.
The database employs a tiered storage approach, keeping fresh and frequently queried data in a hot storage tier, while older data is moved to a cold storage tier as Apache Parquet files.
This columnar format, combined with the Parquet format’s compression-friendly nature, allows for high-ratio compression and efficient storage of high-fidelity data.
Innovative storage optimizations enable it to handle vast amounts of real-time data with unlimited cardinality, supporting high-cardinality use cases such as real-time analytics, observability, and IoT/IIoT applications,
Data Analysis
Our Data Platform offers robust data analysis performance, excelling in flexibility and efficiency for monitoring and visualizing metrics.
Analysis Optimization Techniques: The data platform supports query optimization through label selectors, time range narrowing, and line filters, which reduce data volume and enhance query speed. For instance, selecting precise labels or shorter time ranges minimizes processing time.
Handling Large Datasets: The dat platform handles large datasets. We avoid degraded performance using advanced techniques such as feature extraction and data homogenization.
Real-Time Analysis: New features handling real-time data streaming ensures immediate insights for IoT and operational monitoring.
Use of Artificial Intelligence Tools
AI tools can be used to enhance observability and data analysis.
Machine Learning offers predictive analytics, anomaly detection, and forecasting capabilities across various data source. It enables adaptive alerting, capacity planning, and early anomaly detection.
Key AI features include:
- Sift: An AI-powered diagnostic assistant for incident analysis based in Machine Learning.
- Automated anomaly detection and correlation.
- GenAI for task automation, such as creating PromQL queries and dashboard descriptions.
- AI-driven root cause analysis. Signal correlation to understand relationships between metrics.
- LLM integration for natural language queries and report generation.
These AI tools help streamline workflows, reduce operational costs, and provide deeper insights into system performance and potential issues.
Creation of Predictive Models
Machine Learning enables the creation of predictive models using data from various sources.
The process involves:
- Data Selection: Choose metrics from supported data sources like Prometheus, Loki, or InfluxDB
- Model Training: Use historical data to train the model, learning patterns and trends.
- Forecast Generation: The trained model generates predictions with confidence bounds.
- Visualization: Display forecasts on dashboards using time series graphs or custom panels.
- Tuning: Adjust hyperparameters like changepoint prior scale to optimize model performance.
- Integration: Use forecasts for alerting, capacity planning, and anomaly detection.
- Iteration: Continuously refine models based on new data and performance feedback.
This process leverages data visualization capabilities with machine learning to provide actionable insights and proactive decision-making tools.