MapD’s analytics platform, used to find data insights beyond the limits of typical analytics tools, brings in more location-driven aspects in 4.0 version.
Analytics platform MapD has rolled out their latest update, MapD 4.0, calling it “a major leap forward” for interactive geospatial analytics and interactive location intelligence on a large-scale, tightly integrated with a powerful GPU-based rendering engine.
It enables improved visual interactivity for extensive location intelligence usage such as visually uncovering the relationship between demographic data, spending patterns on a map, uncovering driver behavior patterns from connected vehicle telemetry, and gauging cellular signal strength variances in a city. Also, it offers various improvements enabling enterprise-readiness that offers simplistic support regarding machine learning, access management, and collaboration.
“Many analytics tools aren’t just crumbling under the weight of data, they also lack the capabilities to handle this spatio-temporal data at granular levels,” says Venkat Krishnamurthy, MapD’s Vice President of Product Management.
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MapD pioneered the use of parallel GPU processing for big data analytics in a wide range of fields from operational and geospatial analytics to data science. It is delivered in open cloud and is utilized in telecom, financial services, defense and intelligence, automotive, retail, pharmaceutical, advertising, and academia.
For geospatial analysis, MapD 4.0 further expands on the power of the platform by natively supporting geometry and geographic data types such as points, lines, polygons, and multipolygons, and key spatial operators. Combined with a newly-enhanced rendering engine, users are now able to query and visualize up to millions of polygons and billions of points with unprecedented speed.
As well, MapD 4.0 makes computation-heavy challenges that used to be unpredictable, now possible at lightning fast speed. Rich Sutton, MapD customer and VP of Geospatial at Skyhook, believes that “this simplifies our processing supply chain and opens up huge opportunities for data analysis and enrichment.”
