6. 6
Analytics over Transient Data
Transactional
Data
Behavioral
Data
Edge
Data
Fraud Detection and
Live data marts
Personalization and
Recommendation
Edge Analytics and
Operational
Intelligence
(Financial trades, market
data, shopping carts)
(Clickstream, Geospatial,
Mobile)
(Industrial IoT, device
data, sensors, signal)
8. Decentralized Analytics at
the Source (of data)
8
Unified Real-time/Batch
Low-Latency Infrastructure
(In-Memory Data Grids)
Move analytics to the data, not the other
way around
11. 11
Minimize Airlines Disruptions: Top 5 Largest Worldwide Airline by Fleet
Delay Event
Processing
Analyze and
Automate
Simulate and Adjust
Flight Delay
Propagation Event
Processing
Predict flight delays, passenger
misconnects, and crew scheduling
bottlenecks
Snapshot
from
Trigger
Snapshot Data and build machine
learning simulation models to adjust
flight propagation rules in real-time
Smaller Volume
(100s Gigabytes)
Larger Volume
(10s of Terabytes)
Air Traffic Controller Advisor
Events
12. 12
Minimize Airlines Disruptions: Top 5 Largest Worldwide Airline by Fleet
Solution
• Spark on top of In-Memory Data grid processing 280K flights & 20
million passengers in real-time
• Processing of all flight propagation data under 200 milliseconds (vs. 10
minutes using legacy RDBMS data warehouse)
• Democratize data simulation processing over live data snapshots in real-
time across the enterprise
On-demand
Realtime Data
snapshots
Streaming and
event
processing
13. Check us out at Booth #844!
http://insightedge.io
@InsightEdgeIO @GigaSpaces
Editor's Notes
Hybrid transactional analytics is the convergence of data, insights, and action all together in real-time analytics closed-loop in order to build insight-driven business processes and gain competitve advantages. For many enterprises, this is the panacea of the next phase beyond big data: fast data analytics. However, so many challenges still remain in connecting realtime insights into transactional and operational system.
This session will address the use cases and experience that GigaSpaces leveraged over a decade in moving from big data analytics or low latency transaction processing to operational intelligences and insight-driven machine to machine automation. Case studies will include retail, transportation, airlines, and telecommunications.
Increasingly, this is a matter not just of success but also of survival – the survival of your organization and of you yourself as a senior stakeholder in that organization. We are already seeing leading businesses using big data to disrupt markets and threaten their competitors’ traditional value propositions.
Contextual insights for optimized operations
Transactional Data (Fraud Detection)
Behavioral Data (Web, mobile, clickstream) for channel retargeting and recommendation
Edge Data (sensors, devices) for industrial automation
Data analyzed at the edge can drive automated decisions in true real-time. While still maintaining replicated state.