This document introduces NoSQL and graph databases. It discusses how NoSQL databases can help with complex and dynamic data that is difficult to model. Graph databases are recommended because they provide a flexible data structure and natural query system that closely matches business logic. Neo4j is highlighted as a top graph database due to its ACID compliance, Cypher query language, and large community support. The document provides examples of how graph databases can help with tasks like social network analysis and bioinformatics.

1. Introduction to NoSQL
(Not Only SQL)
By Antonio Castellón :: Multi-Disciplinary Engineer – Computer Science
May, 2015 - for Philip Morris International R&D

2. Problem : Data Complex

3. Problem : Data Complex to Model

4. Problem : Dynamic Data ( Uncertainty )
End User requirements and data itself sometimes generate
different types of uncertainty

5. The NoSQL Jungle

6. Data – NoSQL – Different implementations
CURRENTLY
+150

7. Data - NoSQL – Comparing data structure
Image from: http://highlyscalable.wordpress.com/2012/03/01/nosql-data-modeling-techniques/

8. Data - NoSQL – Compare
98% of the business
requirements
There is still billions of
nodes and relationships

9. Data - NoSQL – Keys to fit
Key-value
store
Column
Store
Document
Store
Graph
Database
Performance High High High Variable
Scalability High High Variable (High) Variable
Flexibility High Moderate High High
Complexity None Low Low High
Functionality Variable(None) Minimal Variable (Low) Graph Theory

10. Data – Our selection
Graph
Databases

11. Data – Graph Databases – Why?
Flexible data structure
Doesn’t matter if the relations will change in the future.
Closer match to business logic

12. Data – Graph Databases – Why?
Natural query system
You tell what you want, not how to get it.
with recursive cluster (party, path, depth)
as ( select cast(@userId as character varying),
cast(@userId as character varying), 1
union
(
select (case
when this.party = amc.userA then amc.userB
when this.party = amc.userB then amc.userA
end), (this.path || '.' || (case
when this.party = amc.userA then amc.userB
when this.party = amc.userB then amc.userA
end)), this.depth + 1
from cluster this, chat amc
where ((this.party = amc.userA and
position(amc.userB in this.path) = 0)
or (this.party = amc.userB and position(amc.userA
in this.path) = 0)) AND this.depth < @depth + 1 )
)
select party, path
from cluster
where not exists (
select *
from cluster c2 where cluster.party = c2.party
and (
char_length(cluster.path) > char_length(c2.path)
or (char_length(cluster.path) =
char_length(c2.path)) and (cluster.path > c2.path)
)
)
order by party, path;
SQL = several hours to be executed
VS
START b = node:User(UserId=‘Manolo')
MATCH (b) --(friend)--(friendoffriend)
RETURN count(friendoffriend)
Cypher Language = 635ms

13. Data - Graph Databases – Why?
Fits very well with complex data

14. Data - Graph Databases – Why?
Fits very well with Bio-Informatics
0.9 Billion
relationsips

15. Data – Graph Databases – Why?
Fast Prototyping and development
We don’t need to lose too much time to define the schema (fine-grained).

16. Data - Graph Databases – What is it?
Properties
Labels
Relationships

17. Data - Graph Databases - Implemented by …

18. Data - Graph Databases – Top 3
Name API Query Methods Consistency Staff (people) /
Community
OrientDB Java Traverser
API, Blueprints,
Rexster
Own SQL-like
Query Language,
Gremlin
ACID, MVCC 3 / Low
Neo4j Java, Python,
JPython, Ruby,
JRuby, JavaScript
(Node.js), PHP,
.NET, Django,
Clojure, Spring,
Scala, or REST
(any language)
Cypher
(native/preferred),
Native Java APIs
(special cases),
Traverser API,
REST, Blueprints,
Gremlin
ACID 42 / Very High
DEX Java, C++, .NET Native Java, C#
and C++ APIs,
Blueprints, Gremlin
Consistency,
durability and
partial isolation
and atomicity
5 / ?

19. Data - Graph Databases - Neo4j customers

20. Data - Graph Database - Neo4j - Partners

21. End
Thanks you for your attention.