The document discusses Amazon Neptune, a fully managed graph database service. It provides examples of how Neptune can be used to query highly connected data through property graphs and RDF models. It also summarizes the key capabilities of Neptune such as its support for popular graph query languages like Gremlin and SPARQL and how Neptune can be used alongside machine learning. Finally, it provides a preview of Neptune's general availability planned for 2018 across multiple AWS regions.

1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Ian Robinson
Specialist Solutions Architect, Data and Analytics, EMEA
Let Me Graph That For You
Amazon Neptune
Charles Ivie
Semantic Solutions Architect, Semantic Integration

2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS Databases
• Strong schema, normalized,
relational data model
• Transactions and joins
• Variable schema, denormalized,
key-value/document data model
• Aggregate-oriented – create,
modify and read individual items

3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Difficult Decisions…
• Complex domain model
• Variable schema (per entity)
• Optional attributes
• Variable structure (across domain)
• Highly connected data
• Queries require joining entities or navigating relationships

4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Navigate a Web of Global Tax Policies
“Our customers are increasingly required to navigate a complex web of global tax policies and
regulations. We need an approach to model the sophisticated corporate structures of our
largest clients and deliver an end-to-end tax solution. We use a microservices architecture
approach for our platforms and are beginning to leverage Amazon Neptune as a graph-based
system to quickly create links within the data.”
Tim Vanderham, Chief Technology Officer, Thomson Reuters Tax & Accounting

5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Building Applications Over Highly Connected Data
Retail Fraud DetectionRestaurant RecommendationsSocial Networks

6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Recommendations Based on Relationships

7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Knowledge Graph Applications
What museums should
Alice visit while in Paris?
Who painted the
Mona Lisa?
What artists have
paintings in The Louvre?

8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Neptune: A Fast, Reliable Graph Database
Optimized for storing and querying highly connected data

9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Neptune: A Fully Managed Graph Database
Fast Reliable Open
Query billions of
relationships with
millisecond latency
6 replicas of your data
across 3 AZs with full
backup and restore
Build powerful
queries easily with
Gremlin and SPARQL
Supports Apache
TinkerPop & W3C
RDF graph models
Easy

10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Leading Graph Models and Frameworks
Open Source Apache TinkerPop
Gremlin Traversal Language
Property Graph Resource Description
Framework (Rdf)
W3C Standard
SPARQL Query Language
• Vertices and edges (nodes
and relationships) with
properties
• Both record-like items
• Triples
• subject – predicate – object

11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Example: Universities
• Institutions
• Organisational structure
• Roles and job titles
• Research, teaching and
administrative staff
• Subjects and course catalogs
• Timetables
• Undergraduate and graduate
populations

12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Find the graduate students who received an
undergraduate degree from the same university
undergraduateDegreeFrom
name: ?
name: ?
University
GraduateStudent
name: ?
Department
memberOf
subOrganisationOf

13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Gremlin
g.V().hasLabel('GraduateStudent').as('student').
out('memberOf').
out('subOrganisationOf').
in('undergraduateDegreeFrom').
where(eq('student'))

14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Gremlin
g.V().hasLabel('GraduateStudent').as('student').
out('memberOf').
out('subOrganisationOf').
in('undergraduateDegreeFrom').
where(eq('student'))

15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Gremlin
g.V().hasLabel('GraduateStudent').as('student').
out('memberOf').
out('subOrganisationOf').
in('undergraduateDegreeFrom').
where(eq('student'))

16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Gremlin
g.V().hasLabel('GraduateStudent').as('student').
out('memberOf').
out('subOrganisationOf').
in('undergraduateDegreeFrom').
where(eq('student'))

17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Gremlin
g.V().hasLabel('GraduateStudent').as('student').
out('memberOf').
out('subOrganisationOf').
in('undergraduateDegreeFrom').
where(eq('student'))

18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Gremlin
g.V().hasLabel('GraduateStudent').as('student').
out('memberOf').
out('subOrganisationOf').
in('undergraduateDegreeFrom').
where(eq('student'))

19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
SPARQL
?univ

20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
SPARQL
?univ

21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
SPARQL
?univ

22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
SPARQL
?univ

23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Neptune High Level Architecture
Bulk load
from S3
Database
Mgmt.

24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Neptune Use Cases
Social Networking Recommendations Knowledge Graphs
Fraud Detection Life Sciences Network & IT Operations

25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Graph is Complementary to ML and Analytics
Amazon
Comprehend
Amazon Simple
Storage Service
(S3)
Amazon
Neptune
Entity Extraction
from RSS Feeds
Load from S3 into
Neptune
Gremlin Traversal
and Visualisation
Graphexp

26. AWS Neptune for
Publishing
Charles Ivie info@semanticintegration.co.uk
Semantic Integration Ltd.

27. We have worked with

28. Editing with Machine Learning / N.L.P

29. Tags from graph taxonomies (DBPedia)

30. Search

31. Categorisation

32. SEO
Google: Best Hotels in London

33. Data
Driven
Pages

34. Recommendation Engines

35. Using Neptune
● 1 AWS Lambda
● 3 Micro-services (Beanstalk)
● 1 S3 Bucket
● 1 Neptune Instance
● DBPedia dataset
● Some ‘Out of copyright fiction publications’

36. Why use Neptune?
● Fully managed service
● Scalable
● Secure
● Highly Available
● Durable
● Compatible with Semantic Web standards

37. Architecture

38. Proof of Concept
● 4954 Lines of code
http://neptune.sparqlr.com/

39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Customers Previewing Amazon Neptune

40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Neptune General Availability
• Planning for 2018
• Four regions
• US East (No. Virginia), US East (Ohio),
US West (Oregon), EU West (Ireland)
• Asia Pacific (Sydney) by the end of
year
• Preview Feature Set Plus:
• Data Endpoint IAM Authentication
• Apache TinkerPop 3.3.2
• Sign-up for the Preview
• https://aws.amazon.com/neptune/

41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS Databases

42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Thank You