www.Objectivity.com Latest Trends in Big Data and Graph Database technologies Brian Clark, VP Product Management on August 16 th , 2012
Jan 14, 2015
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