A high-performance, scalable data infrastructure platform that is purpose- built for fast and effective analysis of spatiotemporal data.
A world of spatiotemporal data
As the volume, velocity and variety of machine data grows exponentially and new sources are added, data processing and analysis becomes more challenging.
Latency is frequently measured in hours or days which impedes the ability to extract timely insights for decision-making.
Streaming live data from moving devices such as mobile phones, GPS devices and vehicles presents further challenges as typical enterprise data architectures are not designed for fast ingest and simultaneous querying of data with spatiotemporal attributes.
Typical challenges working with live data include:
- Indexing billions of small records quickly
- The need to search across multiple columns and attributes at scale
- Unpredictable distribution of data and query loads causing storage hotspots
- Poor storage cache efficiency
Challenges working with live location data:
As data volumes grow and new data sources are added, the complexity, time and cost of data aggregation, ingest, storage and query, make processing impractical.
About the platform
Based on a highly optimized database kernel containing multiple technologies and unique architectural features, the platform provides an unparalled foundation and scalable solution for processing high velocity, high volume spatiotemporal workloads.
State-of-art, thread-per-core architecture, vectorised storage model and user space scheduling of I/O and execution combine with the ability to ingest and simultaneously index streaming workloads, allowing analysts to create immediately queryable datasets and run complex, spatiotemporal queries in seconds instead of minutes and hours.
Index on ingestion at millions of records per second and create instantly queryable datasets for real-time monitoring and alerts.
Layer historic and streaming spatiotemporal data, in different formats to create rich data models at massive scale.
Run complex polygon relationship and geofencing queries in seconds to uncover new insights and guide critical decisions.
Snippet 1 - Add a new observation
Snippet 2 - Find all observations in a polygon
Snippet 3 - Find all unique entities in a polygon over a specific timeframe.
Deep dive into the platform's unique architecture
4-dimensional index design enables diverse and unrelated spatiotemporal datasets to be easily aggregated. Run complex spatiotemporal queries such as polygon relationships and geofencing on 10-100 billion records in seconds.
Thread-per-core architecture with I/O scheduler designed for high dimension indexes uses its visibility and control over storage access and cache to reorder, optimise and reduce storage operations.
Continuous, adaptive, background re-sharding distributes the data workload evenly to mitigate overloads and bottlenecks.
A high-density storage architecture creates a non-POSIX file system that coexists with standard Linux environments to bypass traditional scalability and performance limitations. The platform processes petabytes of data with consistent performance across millions of logical files while implementing additional features that conventional file systems do not support.
Check out the speed and scale of the solution with a synthetic dataset of 1.6 million vehicles in London. The data is based on vehicle number plates with historic movements loaded in time order to realistically simulate a real-time feed of 92.4 billion datapoints.
The platform sits alongside your existing data infrastructure....
Real time engine
Data lakes/ Warehouse
Real time apps
Real time events
Data sources supported
Physical moving objects
Personal devices, such as a smartphone or smart watch
Fleet of vehicles
Industrial or consumer robots or drones
Weather systems, earthquakes
Non physical objects
High volume or automated financial transactions
IP packets and IP traffic patterns
Static sensors that track moving objects
Series of cameras such as number plate recognition
Aerial or satellite imagery
Indexing pixels or tiles to retrieve specific features at scale