Post on 14-Apr-2017
transcript
Geo-Analytics with Apache Spark and In-Memory Data Grids
Copyright © GigaSpaces 2016. All rights reserved.
Ali HodrojVP, Products and Strategy
@ahodrojStrata+Hadoop New York 2016
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In-Memory Computing Product Portfolio
Direct customers300+
Fortune / Organizations
40+ / 500+ Cloud Customers
75+ISVs
25+
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Selected Customers
Geo-Spatial Intelligence
Beyond just maps and visualizations….
GeoSpatial Intelligence: Confluence of 3 Trends
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GeoSpatial Insight is Essential
Financial Services: Detect and prevent fraud by correlating spatial, temporal, and transactional data altogether
Retail: Geospatial data in personalization can help retailers optimize their promotional activity based on customer locations
Insurance: Risk is often tied to location. Insurers can perform risk simulations against vast amounts of data to come up with the right risk model
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Implementing true GeoSpatial insight
Batch + Real-timeData
Convergence
Closed Loop Analytics(Insights trigger
workflows)
Multi-tenant, Geo-
Federated, Scale-out
requires…
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XAP: fast scale-out in-memory data grid
Large-scale data processing framework
Low-Latency GeoSpatial SQL
Apache Spark meets Low-Latency GeoSpatial Processing
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GigaSpaces XAP In-Memory Data Grid
Elastic Scale-out In-Memory Storage (Shared-nothing, Linear scalability, Elastic capacity)
Low latency and High Throughput (co-located ops, event-driven, fast indexing)
High Availability and Resiliency (auto-healing, multi-data center replication, fault tolerance)
Rich API and Query Language (SQL, Spring, Java, .NET, C++)
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Pluggable data initial load
High Performance Spark with OLTP Capabilities
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Pluggable data initial load
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Case Study: Vehicles/Fleet Spatial Analytics
Challenge
• Stream data from 1,000s of Taxis• Actively monitor and generate real-
time notifications• Location-based tracking, Geo-fencing
Solution
• Elastically scale stream processing and transactional apps together
• Real-time operational intelligence through live in-memory data grid
• Extensible and dynamic pricing/routing/fleet rebalancing rules
Edge components
Data Sources
Try it out
http://insightedge.io
@InsightEdgeIO Booth #667