The Database
© Objectivity, Inc. 2014
The Power of Relationships in Big Data
Leon Guzenda - Objectivity, Inc. Silicon Valley NoSQL Meetup - 1/23/14
© Objectivity, Inc. 2014
Overview
• The Problem
• Current Big Data Analytics
• Relationship Analytics
• Leveraging NoSQL
• Big Data Connection Platform
• Solution Use Case Demo!2
© Objectivity, Inc. 2014
Objectivity, Inc.
• Headquartered in San Jose, CA • Over two decades of NoSQL and Big Data experience • Enables complex data virtualization and Big Data
solutions for the enterprise • Software products:
• Objectivity/DB • InfiniteGraph • InfiniteGraph Social App
• Embedded in hundreds of enterprises, government organizations and products, with millions of deployments.
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A Typical Deployment
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Current Big Data Analytics
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The ProblemInformation Overload!
• Making sense of it all takes time and $$$
• Which lead to a rush to Big Data Analytics
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Current Big Data Analytics
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Leveraging NoSQL
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Not Only SQL - Four Main Technologies
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SimpleHighly Interconnected
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Hadoop?
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Objectivity/DB & InfiniteGraph
• Distributed processing with multithreading client processes and simple servers*
• Distributed, segmented Federated Database with a Single Logical View down to fine grain objects
• Tuned for random access and powerful parallel queries
• Excel at handling very large graph structures with built-in relationship analytics
Hadoop:
• Parallel processing using a divide and conquer or split and merge paradigm
• Sharded, distributed file system
• Tuned for sequential scans and simple queries
• Not suitable for highly interconnected data sets (graphs)
* Process workflow could be driven using MapReduce
© Objectivity, Inc. 2014
Incremental Analytic Improvements Aren’t Enough
• All current solutions use the same basic architectural model.
• None of the current solutions has an efficient way to store connections between entities in different silos.
• Most analytic technology focuses on the content of the data nodes, rather than the many kinds of connections between the nodes and in those connections.
• Why? Because most DBMSs are bad at handling relationships.
• Object and Graph Databases can efficiently store, manage and query the many kinds of relationships hidden in the data.
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Relationship Analytics…A SQL Shortcoming
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Table_A Table_B Table_C Table_D Table_E Table_F Table_G
There are some kinds of complex relationship handling problems that SQL wasn't designed for.
© Objectivity, Inc. 2014
…Relationship AnalyticsA SQL Shortcoming
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Table_A Table_B Table_C Table_D Table_E Table_F Table_G
InfiniteGraph - The solution can be found with a few lines of code
A3 G4
© Objectivity, Inc. 2014
Graph Terminology
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● VERTEX: A single node in a graph data structure
● EDGE: A connection between a pair of VERTICES
● PROPERTIES: Data items that belong to a particular Vertex or Edge
● WEIGHT: A quantity associated with a particular Edge
● GRAPH: A network of linked Vertex and Edge objects
Vertex 1 Vertex 2Edge 1
City: San Francisco Pop: 812,826
City: San Jose Pop: 967,487
Road: I-101Miles: 47.8
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Example 1 - Relationship Analytics
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LOGISTICS HEALTHCARE INFORMATICS
MARKET ANALYSIS SOCIAL NETWORK ANALYSIS
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Finding The Links…
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Combatant A
Civilian Q
Situation Y
Civilian P
Bank X
Civilian S
Civilian R
Events/Places People/Orgs Facts
Situation X
Target T
Cafe C S Seen Near TA Banks at X
A Called P
A Seen At Y
A Seen Near X P Emailed S
P Called Q Q Seen Near T
P Called R R Seen Near T
X Paid S
A Eats At
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…Finding The Links…
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Combatant A
Civilian Q
Situation Y
Civilian P
Civilian S
Civilian R
Events/Places People/Orgs Facts
Situation X
Target T
VERTICES EDGES
S Seen Near TA Banks at X
A Called P
A Seen At Y
A Seen Near X P Emailed S
P Called Q Q Seen Near T
P Called R R Seen Near T
X Paid SBank X
Cafe C
A Eats At
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…Finding The Links…
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Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Target T
Seen Near
Emailed
Banks At
Bank X
Civilian S
Seen Near
Called
Civilian R
Seen Near
Paid
Eats At
Cafe C
© Objectivity, Inc. 2014
…Finding The Links…
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Situation X Combatant ASeen Near
Civilian P
Called
Called
Seen At Situation Y
Civilian Q
Target T
Seen Near
Emailed
Banks At
Bank X
Civilian S
Seen Near
Called
Civilian R
Seen Near
Paid
SUSPECTS
NEEDS PROTECTION
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…Finding The Links
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OTHER DATABASE(S)
GRAPH DATABASE
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Example 2 - Finding Patterns in Open Source Data
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● Data Volumes
● Fast-Changing Data
● Sensitivity of Data
● Significance of Data
The Challenges
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Example 3 - Cybersecurity
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Big Data Connection Platform
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Objectivity’s Disruptive Big Data Architecture
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Uses Data Virtualization to hide the nodes and focus on the connections
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InfiniteGraph
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Distributed Parallel Load and Queries
Distributed Parallel Link Finding
Start
Start
Powerful Graph Queries
X
XStart
Finish
Computational and Visualization Plugins
Start
Latency Exceeded
Custom Visualizer
© Objectivity, Inc. 2014
Solution Use Case Demo…
Let’s see InfiniteGraph coupled with Oracle’s NoSQL Solution…
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