MemSQL Merges Geospatial Capabilities with In-Memory Database Technology to Speed Data Analysis

MemSQL, a provider of real-time databases for transactions and analytics, has added geospatial capabilities for its in-memory, distributed, SQL-based database. With the rise of IoT and mobility, nearly all data is location-specific, but geolocation data is often stored and analyzed outside the primary database, causing longer latency and synchronization changes. By bringing together geospatial and operational data in the same database, the goal is to help organizations improve agility for their geospatial analysis.

The early access MemSQL geospatial capabilities are available now and will be generally available in calendar Q2.

“By making geospatial data a primary part of in-memory, operational databases, our customers can rely on one solution to make their data more valuable,” said Eric Frenkiel, MemSQL co-founder and CEO. ““MemSQL is enabling companies to consolidate many niche solutions into fewer, more capable multi-purpose solutions.”

MemSQL integrates geospatial data as a primary data type, making it as easier to use and operate at scale with as much speed and high throughput as any other category of data. By enabling a database that is in-memory, linearly scalable and supports the full range of relational SQL and geospatial functions, MemSQL contends it is helping enterprises to achieve greater database efficiency since geospatial data becomes just another data type.

As an example of the benefits of combining geospatial and operational data, MemSQL cites Esri, a provider of geographic information systems (GIS). Working with MemSQL and Apache Spark, the company was able to aggregate data from 170 million NYC taxi rides around the GPS coordinates of pickups and dropoffs, as well as distance and travel time.

Using this data, Esri can calculate the average speed of a taxi ride and find the best and worst places for traffic jams. Slicing by day of the week, the ebb and flow of traffic during workdays and weekends becomes visible. In practical terms, this data insight can be used by a city planner to redirect traffic at at certain times of the day in an effort to alleviate traffic congestion, and for taxi business owners, this data can help them plan for supply and demand of cabs during times of high or low traffic.

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