PowerOfRelationshipsInBigData_SVNoSQL

The Database

© Objectivity, Inc. 2014

The Power of Relationships in Big Data

Leon Guzenda - Objectivity, Inc. Silicon Valley NoSQL Meetup - 1/23/14


Overview

• The Problem

• Current Big Data Analytics

• Relationship Analytics

• Leveraging NoSQL

• Big Data Connection Platform

• Solution Use Case Demo!2


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


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


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.


…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


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


Example 1 - Relationship Analytics

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LOGISTICS HEALTHCARE INFORMATICS

MARKET ANALYSIS SOCIAL NETWORK ANALYSIS


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


…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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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



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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


…Finding The Links

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OTHER DATABASE(S)

GRAPH DATABASE


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


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


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


Solution Use Case Demo…

Let’s see InfiniteGraph coupled with Oracle’s NoSQL Solution…

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Technology

PowerOfRelationshipsInBigData_SVNoSQL