Better Together: Delivering Graph Value with AWS & Neo4j - Antony Prasad Thevaraj

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This document discusses deploying Neo4j, a graph database platform, on Amazon Web Services (AWS). It provides information on the partnership between Neo4j and AWS, how Neo4j fits into the AWS ecosystem, best practices for architecture and configuration, and steps for deployment using the AWS Marketplace or Neo4j Partners GitHub repository. The document aims to help users get started with running Neo4j on AWS infrastructure.

Data & Analytics

© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
1
N E W Y O R K

© 2023, Amazon Web Services, Inc. or its Affiliates.
Neo4j on AWS
The Leading Graph Data Platform
Antony Prasad Thevaraj
Senior Partner Solutions Architect, AWS

© 2023, Amazon Web Services, Inc. or its Affiliates.
Neo4j and AWS Partnership
● >40% of Neo4j customers run on
AWS
● Member of AWS Partner Network
since 2013
● Collaborative Joint Engineering
● APN Advanced Tier Partner
● Data and Analytics Competency
● SageMaker Service Ready
● AWS Marketplace Seller
● AWS Public Sector Partner
● AWS ISV Workload Migration
● APN Global Startup
● ISV Accelerate

© 2023, Amazon Web Services, Inc. or its Affiliates.
Real-Time
Recommendations
Fraud
Detection
Network &
IT Operations
Master Data
Management
Identity & Access
Management
Risk &
Compliance
REVEALING NEW WAYS TO SOLVE THE WORLD’S
MOST PRESSING BUSINESS PROBLEMS

© 2023, Amazon Web Services, Inc. or its Affiliates.
Neo4j in the AWS Ecosystem
AWS Cloud
Connector for Apache Kafka
Connector for Apache Spark Amazon SageMaker
Amazon Managed Streaming
for Apache Kafka
Amazon EMR
Neo4j Enterprise Edition
Graph Data Science
Graph
Database
Bloom
Data Warehouse
Connector
Amazon Redshift

© 2023, Amazon Web Services, Inc. or its Affiliates.
Neo4j and SageMaker - Knowledge Graphs for Domain
Specific LLMs
Create a knowledge graph in Neo4j. Use an LLM such as Hugging Face on
SageMaker to query it and build conversational systems
1. Amazon Textract and
Comprehend process varied data
sources
2. Entities are written to a Neo4j
knowledge graph with
relationships modeled.
3. Users can then query a
SageMaker LLM model powered
by Hugging Face
4. That converts natural language
queries into Cypher graph query
language
AWS Cloud
AWS CloudFormation Stack
Amazon SageMaker
Neo4j EE
Graph Database
Amazon
Comprehend
Amazon Textract
User Queries
Apps
Structured
Data
Unstructured
Data
Knowledge Discovery
Business Insights
Graph Data
Science

© 2023, Amazon Web Services, Inc. or its Affiliates.
Neo4j and SageMaker - Graph Feature Engineering
Engineer novel features in Neo4j. Use them to improve the accuracy of
supervised learning models built in SageMaker.
1. Import data into Neo4j
Graph Database
2. Generate feature
embeddings in Neo4j
Graph Data Science
3. Export the embedding to
SageMaker Autopilot
4. Train and deploy an
Autopilot model
AWS Cloud
AWS SageMaker Domain
AWS CloudFormation Stack
Amazon S3
SageMaker Default
Bucket
AWS SageMaker Studio
Neo4j EE
AWS SageMaker
Autopilot
Python GDS API
SageMaker API
SageMaker API
Graph Database
Graph Data
Science

© 2023, Amazon Web Services, Inc. or its Affiliates.
Best Practices

© 2023, Amazon Web Services, Inc. or its Affiliates.
Architecture
Bloom
Public subnet Public subnet
VPC
AWS Cloud
Availability Zone 1 Availability Zone 2
NAT gateway NAT gateway
Neo4j Leader Neo4j Follower
Internet
gateway
Public subnet
Availability Zone 3
Neo4j Follower
Auto Scaling
group
NAT gateway
Neo4j Disk Storage
Business Analysts
Data Scientists
Developer
Developer/DBA
Client Access
BOLT: 7687
HTTP: 7474
HTTPS: 7473
DRIVERS
Application
Browser
BOLT: 7687
HTTP: 7474
HTTPS: 7473
BOLT: 7687
Neo4j Disk Storage Neo4j Disk Storage

© 2023, Amazon Web Services, Inc. or its Affiliates.
Recommendations & Considerations
Compute
Starting Out:
- t3.medium or similar
First Data Workload:
- r6i.large
Production Workload:
- r6i.xlarge
Storage
Starting Out:
- 50GB Min. (EBS)
First Data Workload:
- 100GB Min (EBS)
Production Workload:
- 100GB Min (EBS)
Networking
Connectivity:
- Neo4j access
allowed from
0.0.0.0/0 by default
- SSH access can be
specified using a
parameter in
CloudFormation

© 2023, Amazon Web Services, Inc. or its Affiliates.
Deployment

© 2023, Amazon Web Services, Inc. or its Affiliates.
Neo4j on AWS Marketplace
● Neo4j Enterprise
○ AMI and CFT
○ BYOL
○ Graph Database
○ Graph Data Science
○ Bloom
● AuraDB Enterprise
○ DBaaS
○ Annual Commitment
○ Private Offers

© 2023, Amazon Web Services, Inc. or its Affiliates.
Neo4j Partners GitHub
● Deploy with the AWS CLI
● Source code for the IaaS
Marketplace listing
● Simple template that is a good
starting point for customizations

© 2023, Amazon Web Services, Inc. or its Affiliates.
Next Steps
● Try it yourself!
○ AWS Marketplace
○ Neo4j-Partners GitHub
○ AWS Quick Start
● Further Reading
○ Landing Page
○ APN Partner Finder

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Roberto Sannino, Larus Business Automation Nel panorama sempre più complesso dei progetti basati su grafi, LARUS ha consolidato una solida esperienza pluriennale, costruendo un rapporto di fiducia e collaborazione con Neo4j. Attraverso il LARUS Labs, ha sviluppato componenti e connettori che arricchiscono l’ecosistema Neo4j, contribuendo alla sua continua evoluzione. Tutto questo know-how è stato incanalato nell’innovativa soluzione Galileo.XAI di LARUS, un prodotto all’avanguardia che, integrato con la Generative AI, offre una nuova prospettiva nel mondo dell’Intelligenza Artificiale Spiegabile applicata ai grafi. In questo speech, si esplorerà il percorso di crescita di LARUS in questo settore, mettendo in luce le potenzialità della soluzione Galileo.XAI nel guidare l’innovazione e la trasformazione digitale.

GraphSummit Milan - Visione e roadmap del prodotto Neo4j

Neo4j

GraphSummit Milan & Stockholm - Neo4j: The Art of the Possible with Graph

Neo4j

Dr Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.

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