The history of Big Data traces its origin to 1940s; the earliest documented use of the term “information explosion” and today we have Big Data explosion in geography (geospatial data).
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate (Wikipedia). Paleolithic petroglyphs to modern data centers, the human race has always dealt with information. With technology innovation, Moore’s law is becoming irrelevant and Parkinson's Law of Data; “Data expands to fill the space available for storage” is resulting in information overflow or so called Big Data.
The world of Big Data is unfolding dramatically right before us from the amount of data being generated to the way in which it is structured and used. Despite the ever growing interest on “Big Data”, we surprisingly hear little about “Big Geospatial Data.” Nevertheless geospatial data has always been “Big Data”. Thanks to the advancements in geospatial data collection or acquisition such as satellite, remote sensing, global navigation satellite systems, aerial surveys using photographic / digital cameras, sensor networks, LiDAR and now Internet of Things (IoTs), is leading to exponential growth in volume of geospatial data. Big (Geospatial) Data exceeding capacity of current computing systems, presents its own set of opportunities and challenges. Examples of Big (Geospatial) Data include, but not limited to earth observation data, sensor data, location information, spatio-temporal data; which are key inputs for real time monitoring and management class of geospatial applications.
Organisations that generate and or consume geospatial data, suddenly find themselves swimming in so much data that they don't know what to do with them, besides faced with challenges such as capture, query, analysis, visualization, and dissemination of this data.
Contrary to the notion that Big (Geospatial) Data is just about handling lot more data (both structured and un-structured) requiring sophisticated data storage systems, organisations embracing Big (Geospatial) Data fail to take note that, it is also about handling
Volume: unprecedented growth in data volumes
Velocity: rapid increase in the velocity or speed of data creation (generation)
Variety: extension in the variety of data types to be handled
Big (Geospatial) Data is more specifically concerned with the advancing speed in which new information, from an increasing number of diverse data sources and types, confront organisations trying to embrace Big (Geospatial) Data.
As with any Big Data, the three V’s: Volume, Velocity and Variety are the critical challenges with Big (Geospatial) Data as well. With efficient handling of these three V’s or “Small Things of Big (Geospatial) Data”, organisations embracing Big (Geospatial) Data is sure to reap the full benefits.
With FME (Feature Manipulation Engine), the industry standard in spatial data transformation technology, from Safe Software Inc., Canada you are in “Safe” hands for a reliable handshake with the ever growing Big (Geospatial) Data universe.
Volume: With support for several Big Data applications, FME eases the process of migration of voluminous (Volume) legacy or existing data of different types and models from/ to Big Data services or solutions.
Velocity: There is so much of geospatial and non-geospatial data today available for use immediately or in real time as soon as it is collected. In order for such real time data to be of any use, it requires suitable systems in place, to handle the advancing speed in which new information floods the system, from an increasing number of diverse data sources and types. FME’s real-time processing functionality has the ability to immediately respond to events and trigger different FME actions to ensure real-time data is delivered exactly how it's needed in actual real-time.
Variety: FME’s exceptional data conversion, transformation, integration, validation and migration capabilities together with support for 345+ data sources such as BIM/ CAD/ Database/ GIS/ LiDAR/ Raster/ Vector/ Web/ XML/ Sensor file formats and data model barriers; can help organisations solve Big (Geospatial) Data challenges with ease.
With one powerful integration engine and three ways to deploy: FME Desktop, FME Server and FME Cloud, keep ahead of evolving Big (Geospatial) Data, while addressing the three critical V’s or “Small Things of Big (Geospatial) Data”. While FME Desktop lets you connect and transform data in limitless ways, FME Server provides enterprise-level access to FME's powerful capabilities. FME Cloud is the hosted version of FME Server with no hardware required.
Despite several challenges posed by Big (Geospatial) Data, it has the potential to improve operations and make quicker and intelligent decisions. Big (Geospatial) Data when converted, transformed, shared and or integrated appropriately using a platform like FME, can help organisations gain useful insight to increase revenues, better manage its assets and improve operations geospatially, not just in a BIG way but also in a “Safe” way!