SpaceCurve delivers spatial data at the speed of reality. Find out how SpaceCurve integrates with the Hadoop ecosystem to transform the Internet of Everything into operational intelligence.

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Spatial Data
Hadoop Ecosystem
SpaceCurve’s Spatial Data Platform
Integrating with Hadoop

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• Largest datasets are geospatial in nature
– Daily generation of petabytes of data
– Most is not used or simply discarded
• Proliferation of mobile platforms, sensors and
IoT
– More geospatial data will be generated in real-time
• Typical big data solutions can scale to ingest
and store vast quantities of data
– But these are not designed for real-time,
geospatial data

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Devices > People
In 2008, # of internet devices
exceeded # of people on earth
20 - 50 Billion
Estimated # of connected devices by 2020
80% of all data
has spatial attributes*
90% of all mobile
data is location aware*
*According to Gartner

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ü Mobile Platforms
ü Operational Intelligence
ü Sensored World/Digital Business
ü Context Rich Autonomous Systems
ü Smart Machines/M2M
Source: Gartner Technology Trends 2015

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THE WORLD IS A
STATIC MAP
CAPTURING THE
MOTION OF THINGS
REMOTE CONTROL
OF THINGS
THINGS TALK TO
EACH OTHER
THINGS BEHAVE
INTELLIGENTLY
Map coordinates of points
of interest cataloged and
described on the Internet.
Packages have passive
sensors, we can track on
web and know where they
passed checkpoints.
UAVs used as remote
sensing platforms for
emergency response.
Aircraft optimize fuel
consumption in real-time
using data from internal and
external sensor networks.
Large fleets of autonomous
vehicles adapting to weather
conditions and traffic
congestion.
EXAMPLES

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• Hadoop’s open source platform has become synonymous
with big data processing
• Core ecosystem:
– Distributed file system for data storage (HDFS)
– Distributed processing of data at scale (MapReduce)
– Batch-oriented job execution
• Hadoop-based solutions excel at:
– Ingesting and data warehousing multiple sources of data
– Creating and updating analytical dashboards on a weekly, daily or
hourly basis
– Providing insights from historical data that apply to future
scenarios

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• Hadoop ecosystem can scale to geospatial storage requirements
• HDFS not efficient for organizing and analyzing these data models as:
– Geospatial data does not have a predictable, uniform distribution
– Hash functions can transform unpredictable, non-uniform
distributions do not preserve nor expose geospatial biases and
relationships efficiently
• Results:
– Reduction in parallelism and efficiency of geospatial analysis
– Inability to implement computational geometry needed for
geospatial analytics

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CONTINUOUS HIGH-VELOCITY data ingestion rates are far beyond the
limits of traditional spatial analysis platforms.
SPATIAL ANALYTICS required for high-value Internet of Everything
applications are not supportable on popular big data platforms.
REAL-TIME operational analysis requirements preclude the use
of batch-oriented platforms.
DATA VOLUME greatly exceeds capacity of platforms designed for real-time
analysis of human-generated sources.

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• SpaceCurve has created the first purpose-built
platform from the ground up:
– Designed for organizing multiple streams of very large scale geospatial
data
– Optimized for analyzing data in real-time
– Eliminates limitations on geospatial data inherent in other platforms
• The SpaceCurve platform makes it possible to:
– Collect and fuse multiple sources of data in real-time and immediately
streaming it to an application
– Allow continuous queries and analytics to be run with second and sub-
second responses
– Provide insights from real-time data that can apply to current,
immediate scenarios

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CONTINUOUS
HIGH-VELOCITY
INGESTION
COMPLEX SPATIAL
DATA TYPES
OPERATIONS
EXTREME DATA
VOLUMES
REAL-TIME QUERY
EXECUTION
ANALYSIS

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• Integration at the HDFS layer
• Enables all current systems and tools to be
utilized in their normal workflows
• Leverages existing investments and enables
real-time geospatial use cases
• Build combined workflows that operate in
parallel or where Hadoop components can
call out queries into SpaceCurve

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• Additional resources can be found below:
– Github – https://github.com/SpaceCurve/hadoop
• This resource outlines the mechanics of export/import
between SpaceCurve and Hadoop and includes a step-
by-step tutorial using California earthquake data
– SpaceCurveVM – available upon request
• This resource lets a user install the SpaceCurve system
loaded with sample data and use SpaceCurve SQL to
query the data

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ESRI
Tools
HDFS
MapReduce
Hive
GeoJSON
Mapper
Reducer
Hive
SQL
SpaceCurve
HTTP/JSON
Hadoop
Ecosystem

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