www.Objectivity.com

Latest Trends in Big

Data and Graph

Database technologies

Brian Clark, VP Product Management on August 16th, 2012

Overview

• The Big Data Problem

• Current Big Data Analytics

• NoSQL Technologies

• Relationship Analytics

• InfiniteGraph and NoSQL DB

The Big Data Problem

The Big Data Problem

Information Overload!

Making sense of it all takes time and $$$

•Volume - vast amount of data

•Velocity - rate of input, rate of change

•Variety – structured, un-structured, semi-structured

•Value –analytics to gain understanding from the data and relationships

•Veracity – truth or meaning of the data and relationships

A Typical “Big Data” Analytics Setup

Data Aggregation and Analytics Applications

Commodity Linux Platforms and/or High Performance Computing Clusters

Structured Semi-Structured Unstructured

Graph

DB

Object

DB Doc DB

K-V

Store Hadoop

Column

Store

Data

W/H RDBMS

Incremental Improvements Aren’t Enough

All current solutions use the same basic architectural model • None of the current solutions have a 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 the data in those connections

• Why? Because relational and most NoSQL solutions are bad at handling

relationships. • Object and Graph databases can efficiently store, manage and query the

many kinds of relationships hidden in the data.

NoSQL Technologies

• Users choose between four different primary technologies for different

purposes: – Key-Value Stores

– “Big Table” Clones

– Document Databases

– Object and Graph databases (including InfiniteGraph)

• Many implementations sacrifice consistency (ACID transactions, CAP

– eventual consistency) for performance.

• Technologies such as Objectivity/DB and InfiniteGraph offer ACID

transactions, with consistency and performance.

Not Only SQL – a group of 4 primary technologies

The NoSQL Market

Relationship Analytics

Example 1 - Market Analysis The 10 companies that control a majority of U.S. consumer goods brands

Example 2 - Demographics Used in social network analysis, marketing, medical research etc.

Example 3 - Seed To Consumer Tracking

?

Example 4 - Ad Placement Networks

Smartphone Ad placement - based on the the user’s profile and location data

captured by opt-in applications. • The location data can be stored and distilled in a key-value and column store

hybrid database, such as Cassandra • The locations are matched with geospatial data to deduce user interests. • As Ad placement orders arrive, an application built on a graph database such

as InfiniteGraph, matches groups of users with Ads: • Maximizes relevance for the user. • Yields maximum value for the advertiser and the placer.

Example 5 - Healthcare Informatics

Problem: Physicians need better electronic records for managing patient data on a global

basis and match symptoms, causes, treatments and interdependencies to improve

diagnoses and outcomes. • Solution: Create a database capable of leveraging existing architecture using NOSQL tools

such as Objectivity/DB and InfiniteGraph that can handle data capture, symptoms, diagnoses, treatments, reactions to medications, interactions and progress.

• Result: It works: • Diagnosis is faster and more accurate • The knowledge base tracks similar medical cases. • Treatment success rates have improved.

The Polyglot Approach

SUMMARY: A Polyglot Approach Works Best...

PROBLEM

LANGUAGE REPOSITORY

ANALYTICS

GRAPH TOOLS BI TOOLS VISUAL ANALYTICS

...SUMMARY: A Polyglot Approach Works Best

InfiniteGraph

The Big Data Connection Platform