Graph databases address one of the great macroscopic business trends of today: leveraging complex and dynamic relationships in highly connected data to generate insight and competitive advantage. Whether we want to understand relationships between customers, elements in a telephone or data center network, entertainment producers and consumers, or genes and proteins, the ability to understand and analyze vast graphs of highly connected data will be key in determining which companies outperform their competitors over the coming decade. In this session, I am going to cover following graph database concepts mainly w.r.t Neo4j. High level view of Graph Space Power of Graph Databases Data Modeling with Graphs Cypher : Graph Query language Building a Graph Database Application Graphs in Real World / Common Use cases Predictive Analysis with Graph Theory

1. Using Graph Databases For Insights
Into Connected Data
Gagan Agrawal
Xebia India
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2. SOFTWARE DEVELOPMENT DONE RIGHT
Netherlands | USA | India | France | UK

3. Agenda
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High level view of Graph Space
Comparison with RDBMS and other NoSQL
stores
Data Modeling
Cypher : Graph Query Language
Graph Database Internals
Graphs In Real World
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4. What is a Graph?
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6. What is a Graph?
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A collection of vertices and edges.
Set of nodes and the relationships that connect
them.
Graph Represents 
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Entities as NODES
The way those entities relate to the world as
RELATIONSHIP
Allows to model all kind of scenarios
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System of road
Medical history
Supply chain management
Data Center
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9. High Level view of Graph Space
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Graph Databases - Technologies used primarily
for transactional online graph persistence –
OLTP.
Graph Compute Engines - Tecnologies used
primarily for offline graph analytics - OLAP.
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10. Graph Databases
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Online database management system with Create, Read, Update, Delete
methods that expose a graph data model.
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Built for use with transactional (OLTP) systems.
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Used for richly connected data.
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Querying is performed through traversals.
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Can perform millions of traversal steps per
second.
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Traversal step resembles a join in a RDBMS
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11. Graph Database Properties
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The Underlying Storage : Native / Non-Native
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The Processing Engine : Native / Non-Native
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12. Graph DB – The Underlying Storage
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Native Graph Storage – Optimized and designed
for storing and managing graphs.
Non-Native Graph Storage – Serialize the graph
data into a relational database, an object oriented
database, or some other general purpose data
store.
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14. Graph DB – The processing Engine
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Index free adjacency – Connected Nodes
physically point to each other in the database
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18. Power of Graph Databases
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Performance
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Flexibility
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Agility
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19. Comparison
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Relational Databases
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NoSQL Databases
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Graph Databases
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20. Relational Databases Lack
Relationships
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Initially designed to codify paper forms and
tabular structures.
Deal poorly with relationships.
The rise in connectedness translates into
increased joins.
Lower performance.
Difficult to cater for changing business needs.
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23. NoSQL Databases also lack
Relationships
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NOSQL Databases e.g key-value, document or
column oriented store sets of disconnected
values/documents/columns.
Makes it difficult to use them for connected data
and graphs.
One of the solution is to embed an aggregate's
identifier inside the field belonging to another
aggregate.
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Effectively introducing foreign keys
Requires joining aggregates at the application
level.
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24. NoSQL DB
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Relationships between aggregates aren't first
class citizens in the data model.
Foreign aggregate "links" are not reflexive.
Need to use some external compute infrastructure
e.g Hadoop for such processing.
Do not maintain consistency of connected data.
Do not support index-free adjacency.
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27. Graph DB
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Find friends-of-friends in a social network, to a
maximum depth of 5.
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Total records : 1,000,000
Each with approximately 50 friends
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29. Data Modeling with Graph
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30. Data Modeling
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“Whiteboard” friendly
The typical whiteboard view of a problem is a
GRAPH.
Sketch in our creative and analytical
modes, maps closely to the data model inside the
database.
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32. Cypher : Graph Query Language
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Pattern-Matching Query Language
Humane language
Expressive
Declarative : Say what you want, now how
Borrows from well know query languages
Aggregation, Ordering, Limit
Update the Graph
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33. Cypher
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Cypher Representation :
(c)-[:KNOWS]->(b)-[:KNOWS]->(a), (c)-[:KNOWS]->(a)
(c)-[:KNOWS]->(b)-[:KNOWS]->(a)<-[:KNOWS]-(c)
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34. Cypher
START c=node:user(name='Michael')
MATCH (c)-[:KNOWS]->(b)-[:KNOWS]->(a), (c)[:KNOWS]->(a)
RETURN a, b
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35. Other Cypher Clauses
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WHERE
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CREATE and CREATE UNIQUE
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Create nodes and relationships
DELETE
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Provides criteria for filtering pattern matching
results.
Removes nodes, relationships and properties
SET
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Sets property values
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36. Other Cypher Clauses
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FOREACH
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UNION
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Performs an updating action for graph element in
a list.
Merge results from two or more queries.
WITH
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Chains subsequent query parts and forward
results from one to the next. Similar to piping
commands in UNIX.
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37. Comparison of Relational and Graph Modeling
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43. Graph Database Internals
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44. Non Functional Characteristics
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Transactions
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Fully ACID
Recoverability
Availability
Scalability
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45. Scalability
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Capacity (Graph Size)
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Latency (Response Time)
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Read and Write Throughput
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46. Capacity
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1.9 Release of Neo4j can support single graphs
having 10s of billions of nodes, relationships
and properties.
The Neo4j team has publicly expressed the
intention to support 100B+
nodes/relationships/properties in a single
graph.
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47. Latency
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RDBMS – more data in tables/indexes result in
longer join operations.
Graph DB doesn't suffer the same latency
problem.
Index is used to find starting node.
Traversal uses a combination of pointer chasing
and pattern matching to search the data.
Performance does not depend on total size of the
dataset.
Depends only on the data being queried.
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48. Throughput
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Constant performance irrespective of graph size.
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49. Graphs in the Real World
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50. Common Use Cases
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Social
Recommendations
Geo
Logistics Networks : for package routing, finding shortest
Path
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Financial Transaction Graphs : for fraud detection
Master Data Management
Bioinformatics : Era7 to relate complex web of information
that includes genes, proteins and enzymes
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Authorization and Access Control : Adobe Creative
Cloud, Telenor
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53. Thank You
Xebia India
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54. BigData & Real Time Analytics
Services
Visualization (Tableau)
Analytics Framework (Mahout)
Integration (Sqoop, Flume , Storm)
Hadoop Powered Solutions (Pig, Hive, Oozie,
Hbase Impala) (Solr, Elastic Search)
Core Hadoop
(HDFS, MapReduce,Zookeeper, Cloudera
Trainings
- Cloudera Data Analyst /
Developer / Admin
Training
Products
- Divolte
- Wearable Sensors
Solutions
- Big data warehousing
- Scalable big data etl
- High volume web
analytics

55. Contact us @
Websites
www.xebia.in
www.xebia.com
www.xebia.fr
Xebia India
infoindia@xebia.com
Thought
Leadership
Htto://xebee.xebia.in
http://blog.xebia.com
http://podcast.xebia.com