This document discusses Amazon Neptune, a fully managed graph database service. It provides an overview of graph databases and their advantages over traditional databases for modeling connected data. It then describes Amazon Neptune's key features, like automatic scaling, high availability across Availability Zones, integration with open standards like Gremlin and SPARQL, and ease of use on AWS. Examples are given showing how to model and query graph data using Gremlin and SPARQL. Finally, it discusses Amazon Neptune's architecture and roadmap for general availability later in 2018.

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Andi Gutmans
General Manager, Amazon Neptune, Amazon Web Services
Brad Bebee
Principal Product Manager, Amazon Neptune, Amazon Web Services
Big Data and Analytics
Connecting the dots: How Amazon Neptune and
Graph Databases can transform your business

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‫מ‬ַ‫ט‬ָ‫ר‬ָ‫ה‬
noun | pur·pose | ˈpər-pəs
The reason for which something is done or created
or for which something exists

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Traditional application characteristics
HR Payroll
CRM ERP
…
Users 100s-1000s
Data volume GB-TB
Locality HQ
Performance Seconds
Request Rate Tens of thousands
Access Internal servers, PCs
Scale Up
Economics Pay up front
Developer Access days/weeks/months

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Cloud application characteristics
Users 1M+
Data volume TB-PB-EB
Locality Global
Performance Milliseconds-Microseconds
Request Rate Millions
Access Mobile, IoT, Devices
Scale Up-Out-In
Economics Pay as you go
Developer access Instant API access

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Purpose built for modern applications

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Database characteristics
Referential
integrity with
strong
consistency,
transactions, and
hardened scale
GraphKey-value Document
;
Time SeriesRelational
Low-latency key
based queries with
high throughput
and fast ingestion
of data
Indexing and
storing documents
with support for
query on any
property
Creating and
navigating
relations between
data easily and
quickly
Time-stamped
data with large
range-scans for
summarization
and processing
Complex query
support via SQL
Simple query
methods with
filters
Simple query with
filters, projections
and aggregates
Easily express
queries in terms of
relations
Computational
support for
summarized
results

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AWS Databases and Analytics
B r o a d a n d d e e p p o r t f o l i o , p u r p o s e - b u i l t f o r b u i l d e r s
DW | Big Data Processing | Interactive
Business Intelligence & Machine Learning
Data Movement
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
QuickSight
Relational Databases
RDS
Aurora
Data Lake
S3/Glacier Glue
(ETL & Data Catalog)
SageMaker
Non-Relational Databases Analytics
DynamoDB
ElastiCache
(Redis, Memcached)
Neptune
(Graph)
Redshift EMR Athena
Kinesis
Analytics
Elasticsearch
Service
Real-time
Comprehend

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Highly connected data
Graph
Creating and
navigating
relations between
data easily and
quickly
Easily express
queries in terms of
relations

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‫ש‬‫י‬‫מ‬‫ו‬‫ש‬‫י‬‫ם‬‫ל‬‫מ‬‫ב‬‫נ‬‫י‬‫נ‬‫ת‬‫ו‬‫נ‬‫י‬‫ם‬‫ג‬‫ר‬‫פ‬‫י‬‫י‬‫ם‬
‫ר‬‫ש‬‫ת‬‫ו‬‫ת‬‫ח‬‫ב‬‫ר‬‫ת‬‫י‬‫ו‬‫ת‬
‫מ‬‫ד‬‫ע‬‫י‬‫ה‬‫ח‬‫י‬‫י‬‫ם‬ ‫מ‬‫ע‬‫ר‬‫כ‬‫ו‬‫ת‬‫מ‬‫י‬‫ד‬‫ע‬‫ו‬‫ר‬‫ש‬‫ת‬‫ו‬‫ת‬‫ז‬‫י‬‫ה‬‫ו‬‫י‬‫ה‬‫ו‬‫נ‬‫א‬‫ו‬‫ת‬
‫ה‬‫מ‬‫ל‬‫צ‬‫ו‬‫ת‬ ‫ג‬‫ר‬‫פ‬‫י‬‫ם‬‫ש‬‫ל‬‫מ‬‫י‬‫ד‬‫ע‬

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Recommendations based on relationships

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Knowledge graph applications
What museums should Alice
visit while in Paris?
Who painted the Mona Lisa?
What artists have paintings
in The Louvre?

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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.”
said Tim Vanderham, chief technology officer, Thomson Reuters Tax & Accounting

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Challenges Building Apps with Highly Connected DataThe challenges of building apps with highly
connected data using a relational database
Unnatural for
querying graph
Inefficient
graph processing
Rigid schema inflexible
for changing data

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Different approaches for highly connected data
Purpose-built for a business process
Purpose-built to answer questions about
relationships

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A graph database is optimized for efficient storage
and retrieval of highly connected data

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Leading graph models and frameworks
Open Source Apache TinkerPop™
Gremlin Traversal Language
W3C Standard
SPARQL Query Language
RESOURCE DESCRIPTION
FRAMEWORK (RDF)
PROPERTY GRAPH

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A highly connected university example

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Find all of the graduate students who received an
undergraduate degree from the same university
Undergraduate Degree
From
name: ?
name: ?
University
Graduate Student
name: ?
Department
Member Of
subOrganizationOf

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Sample Property Graph data in csv format

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Gremlin: Find all of the graduate students who received
an undergraduate degree from the same university

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Property Graph CSV data example result

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RDF data extract

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SPARQL: Find all of the Graduate students who received
an undergraduate degree from the same university
?univ

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RDF triples matching the SPARQL query triple pattern

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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
Visualization
Graphexp

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Challenges of existing graph databases
Difficult to maintain
high availability
Difficult to scale
Limited support for
open standards
Too expensive

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Amazon Neptune
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

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Amazon Neptune high level architecture
Bulk load
from S3
Database
Mgmt.

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Fully managed service
Easily configurable via the Console
Multi-AZ High Availability, ACID
Support for up to 15 read replicas
Supports Encryption at rest
Supports Encryption in transit (TLS)
Backup and Restore, Point-in-time
Recovery
B E N E F I T S

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AMAZON NEPTUNE: VPC DEPLOYMENT
• Secure deployment in a VPC
• Increased availability through
deployment in two subnets in two
different Availability Zones (AZs)
• Cluster volume always spans three AZ
to provide durable storage
• See the Amazon Neptune
Documentation for VPC setup details

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AMAZON NEPTUNE READ REPLICAS
Availability
• Failing database nodes are automatically
detected and replaced
• Failing database processes are
automatically detected and recycled
• Replicas are automatically promoted to
primary if needed (failover)
• Customer specifiable fail-over order
AZ 1 AZ 3AZ 2
Primary
Node
Primary
Node
Primary
Master
Node
Primary
Node
Primary
Node
Read
Replica
Primary
Node
Primary
Node
Read
Replica
Cluster
and
Instance
Monitoring
Performance
• Customer applications can scale out read
traffic across read replicas
• Read balancing across read replicas

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Customers previewing Amazon Neptune

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Neptune General Availability
• Planning for 2018
• Four regions
• US East (No. Virginia), US East
(Ohio), US West (Oregon), EU West
(Dublin)
• Preview Feature Set Plus:
• Data Endpoint Authentication
• Assumed Roles Bulk Loading
• Sign-up for the Preview
• https://aws.amazon.com/neptune/

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Thank you!
Andi Gutmans
gutmans@amazon.com
General Manager
Amazon Neptune
Brad Bebee
beebs@amazon.com
Principal Product Manager
Amazon Neptune