Amsterdam - The Neo4j Graph Data Platform Today & Tomorrow

© 2022 Neo4j, Inc. All rights reserved.
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The Neo4j Graph Data
Platform
Today & Tomorrow
Dr. Jim Webber
Chief Scientist

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Exponential Growth in Connected Data
Connected data
use-cases are
exploding in numbers
and in depth
Data models the real
world. Therefore data is
becoming increasingly
connected.
The world is
becoming
increasingly
connected

Graphs Embody Real World Context
• Easily understood
• Easily adapted
• Streamlines collaboration between
business and IT
Context: Faster Insights and Deeper Understanding
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Relational Databases
Don’t handle relationships well
● Not well adapted to interconnected semi-structure
● Schema rigidity limits business agility
● Relationship queries require painful SQL & joins
● Practically limited to three degrees of separation
Other NoSQL Databases
Don’t handle relationships at all
● Built to scale simple data (store & retrieve)
● No language or structures for handling relationships:
joins done through the application
● Lack of ACID makes graph transactions impossible
Graph Databases
Natively store, query relationships
● Data model purpose-built for graphs
● Powerful balance of schema flexibility & structure
● Highly powerful & productive query language
● Queries run 1000x faster at scale, up to 1000+
degrees of separation
Graph Databases Unlock New Data Possibilities
“We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with
queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that
was previously impossible.” --- Volker Pacher, Senior Staff Engineer
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The Neo4j Graph Data Platform
Runs Anywhere
Deploy as-a-Service (AuraDB) or
self-hosted within your cloud of
choice (AWS, GCP, Azure) via their
marketplace, or on-premises.
Development Tools &
Frameworks
Tooling, APIs, query builder,
multi-language support for
development, admin, modeling,
and rapid prototyping needs.
Data Science and Analytics
Explorative tools, rich algorithm library,
and Integrated supervised Machine
Learning framework.
Native Graph Database
The foundation of the Neo4j platform;
delivers enterprise-scale and
performance, security, and data
integrity for transaction and analytical
workloads.
Graph Query Language
Cypher & openCypher; Ongoing
leadership and standards work (GQL)
to establish lingua franca for graphs.
Discovery & Visualization
Code-free querying, data modeling and
exploration tools for data scientists,
developers, and analysts.
Ecosystem & Integrations
Rich ecosystem of tech and
integration partners. Ingestion tools
(JDBC, Kafka, Spark, BI Tools, etc.) for
bulk and streaming needs.
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Neo4j: A Rich History of Graph Innovation
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2020 - 2022
● Graph-RBAC Security, the First and
Most Advanced of Its Kind
● First Native & Fully Integrated Graph
Data Science Offering
● First In-Graph Machine Learning
Technology
● Neo4j Fabric, the First Enterprise
Graph Scaling Architecture for
Sharding & Federation
2015 - 2019
● openCypher: the De Facto Open
Source Graph Standard
● Graph Algorithms for Data Science
● Bloom for Rich Data Visualization and
Exploration
● Active Participant in ISO GQL
Standard, initiated by Neo4j
● AuraDB: First Native Graph DB
as-a-Service
2010 - 2014
● Pioneered the Graph Database Category
● First Native Graph Database:
Open Source, Built for Property Graphs
● Introduced Cypher Graph Query
Language
● Evolved Property Graph Model with
Labels, Geospatial & More
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Guiding Beacons
Performance at Scale
"Minutes to Milliseconds”
Performance: Queries that
take seconds/minutes/hours
run in milliseconds on Neo4j
Business impact: enables entirely
new forms of value creation, not
otherwise possible
Agility - Flexible Schema
Flexible schema enables the
database to adapt to an
evolving business, rapidly and
without disruption
Business impact: projects deliver
more, in less time, with less risk
Developer Productivity
Cypher enables more with less:
10x less code than SQL. Direct
path to an ISO standard.
World’s largest community of
skilled graph developers
Business impact: projects deliver
more, in less time, with less risk
Resource Eﬃciency
Often requires 10x less resources
than other graph databases,
particularly those built on
relational, NoSQL, and RDF
databases
Business Impact: signiﬁcant
bottom-line savings & TCO
reduction
Transactions Analytics
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Neo4j Fabric
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UNWIND demo.graphIds() AS gid
CALL {
USE demo.graph( gid )
MATCH ( u:User )
RETURN max( u.age ) AS localMaxAge
}
RETURN MAX( localMaxAge ) AS globalMaxAge
Unlimited
Scalability for
Sharded and
Federated Graphs
Users queries are executed in
parallel on any graph. Graph
selection can be pruned by
sharding logic. Results are
retrieved and aggregated, then
returned to the user or written
to other graphs.

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30 Billion
1.2 Trillion!
128 Billion
Neo4j Fabric: Scaling Up & Scaling Out!

FAST
● Real-time Performance at Scale
● Automatic Upgrades, Patches, Backups
● Scale on Demand, No Downtime
● High Availability
● Multi Cloud, Any Region
● Enterprise-grade Security
● Simple Capacity Based Pricing
● 24/7 Premium Support
FAST
FLEXIBLE RELIABLE EASY
Self-hosted Available
AuraDB Coming Soon
Fully-managed Cloud Service
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Free
Professional
Enterprise
Self-Start
For
Developers
For
Data
Scientists
Graph Database as a Service Graph Data Science as a Service
Neo4j as a Service Offerings
Enterprise
2H-2022
EAP EOY-2022
2H-2022
EAP, 2H-2022 GA

Aura: Enterprise Grade Security & Management
Single Sign-On
Cyber Academy
Code Scanning &
Pentesting
Secure Development
Practices
Dedicated VPC
AWS PrivateLink
Private Service Connect
(GCP - 2H 2022)
Role- & Schema-Based
Access Control
Node, Edge, and
Traversal Level
Granularity
Encryption
(At-Rest, In-Transit,
and Intra Cluster)
TLS 1.2/1.3, with
encrypted bucket storage

Neo4j Meets All Your Regulatory Compliance Needs
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SOC 2 Type I
ISO 27001
Certified
Coming
H2 2022

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New! Data Warehouse Connector
● Make it easier than ever to move data between your
data warehouse and Neo4j
● Support for Snowflake, Google BigQuery, Amazon
RedShift, and Azure Synapse
● Use as a:
○ Spark Submit Job (provide JSON configuration)
○ Simplified Scala/Python API
Data Warehouse
Connector

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Full Breadth of Connector & Language Support
Neo4j BI
Connector
Apache Spark
Connector
Apache Kafka
Connector
Data Warehouse
Connector
Java Python .NET
JavaScript Go

Neo4j Graph Data Tools For All
Access your Neo4j database locally
Access your Neo4j database from
your browser
Visualize and explore your
Neo4j database
Build intelligent applications faster
with a Neo4j graph database

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Graph Data Science
Rik van Bruggen
VP of Sales Enablement & Operations, EMEA

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Belgium
is a
BEER-
laden
venue
http://blog.bruggen.com/2021/10/rebeergraph-importing-belgian-beergraph.html

What’s
Unusual?
What’s
Important?
What’s
Next?
Graph Data Science Helps
Make Sense of Your Data Relationships
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Exploring the hidden patterns and features in your data

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Graphs Contain Implicit Knowledge
Which of the colored
nodes would be
considered the
most ‘important' ?

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Graph Algorithms Help Unlock This Knowledge
D
D has the highest valence
This is the most connected individual in the
network. If importance is how well you are
personally known you pick D.
G has the highest closeness centrality (0.52)
Information will disperse through the network
more quickly through this individual. If you need
to get a message out rapidly, choose them.
G
I has the highest betweenness centrality (0.59)
This person is an efficient connector of other people.
Risk of network disruption is higher if you lose this
individual.
I
Most Important?

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The Domains of Graph Data Science
Graph Native
Machine Learning
Learn features in your graph
that you don’t even know are
important yet using
embeddings.
Predict links, labels, and
missing data with in-graph
supervised ML models.
Identify associations,
anomalies, and trends using
unsupervised machine
learning.
Graph Algorithms
Knowledge Graphs
Find the patterns you’re looking
for in connected data

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The easiest graph
data science
platform
Easy to use
● Automated MLOps
● Runs Anywhere
● Cloud Commitments
Built by data
scientists, for data
scientists
Data Scientists
● Native Python Client
● 65+ Graph Algos
● ML Pipelines
Go to production
with speed, security,
and scale
Enterprise Ready
● 8M Objects/sec Load
● RBAC Security
● Designed to Scale
Fits into your data
stack and pipeline
Ecosystem
● BI tools, Apache Spark,
Kafka, Data Warehouses
● Vertex AI, SageMaker,
Synapse
Neo4j Graph Data Science
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The Neo4j Native Graph Catalog
• Automates data transformations
• Experiment with different data
sets, data models
• Fast iterations & layering
• Production ready features,
parallelization & enterprise
support
• Ability to persist and version
trained models
A graph-specific analytics workspace that’s mutable – integrated with
Neo4j’s native-graph database
Mutable In-Memory
Workspace
Graph Projection
Native Graph Store

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The Largest Catalog of Graph Algorithms
Pathfinding &
Search
Centrality &
Importance
Community
Detection
Supervised
Machine Learning
Heuristic Link
Prediction
Similarity Graph
Embeddings
…and more
Over 65 Pretuned, Parallelized Algorithms

From Chaos to
Structure:
Neo4j Graph Data
Science is Changing
How Machine Learning
Gets Done
Graph Embeddings summarize the enhanced
explicit knowledge of a graph
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“50% of Gartner inquiries on the topic
of AI involve discussion of the use of
graph technology.”
Top 10 Tech Trends in Data and Analytics 2021
AI Research Papers
Featuring Graph
Source: Dimensions Knowledge System

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Real World Data, Real World Fraud Detection
• Real, anonymized customer data set
• Using Neo4j Graph Data Science
• Blog post with sample code available
https://neo4j.com/developer-blog/exploring-fraud-detection-neo4j-graph-data-science-summary/
Exploring Fraud Detection With Neo4j &
Graph Data Science
Zach Blumenfeld
Data Science Product Specialist, Neo4j
87%
More fraud risks
detected!

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Logistics and Supply Chain
Plan maritime routes based on distances, costs,
and internal logic.
Results:
● Subsecond maritime routes planning
● Reduce global carbon emissions 60,000 tons
● 12-16M ROI for OrbitMI customers

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New in Neo4j Graph Data Science
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Machine Learning Pipelines
Graph Native ML Pipelines:
● Node Classification
● Node Regression (new!)
● Link Prediction
Trained Models & the Model Catalog:
● Save, persist & publish trained models
● Instantly apply for prediction on new and
existing data in the graph
Guided & Automated Pipeline
Features:
● Data splitting & rebalancing
● Feature engineering
● Model evaluation and
selection
● Automated hyperparameter
tuning (new!)
What we do : Link
Prediction Example
Define a pipeline, add the steps you want to perform, and we handle the rest for you.

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New! Apache Arrow Integration
● Import and export massive
graphs directly in and out
● Fast graph projections
(from external sources)
● Database creation & algorithm
result export
Up to 30 million objects/second

New! Native Graph Data Science
Python Client
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● Simplifies workflows for data scientists
● Run Graph Data Science algorithms just
like any Python function
● Eliminates the need for transaction
functions for data science
● Pythonic features support for graph and
model objects

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New!
Graph Data Science
As-a-Service
● Handles all DB
management for you
● Simple to scale up or down
● Paused instance to save
● Starts at $1/hour
Enterprise ready, with fully
managed infrastructure,
updates, and security patches

New! Explore Graph Algorithms Directly in Bloom
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Looking Ahead
Chris Shelmerdine
Product Manager

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But first… new in Neo4j Graph Data
Platform!
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Data Importer: Model & Load Your First Graph
● Intuitive data modeling UI
● Map flat files to graphs
● Built to import up to millions
of records
● Import directly Neo4j Aura or
self-managed
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Neo4j Ops Manager
Manage all Neo4j deployments centrally
Dashboard covering top metrics for
OS, DBMS, and Database
Dedicated UIs for managing
Role Based Access Control,
Upgrades, and more

Rich
Capabilities
We’re always innovating with
cutting edge features to
power your business
We make graph easy, so you
can focus on building
business value
Frictionless
Experience
Enterprise
Grade
We prioritize trust in the
platform; secure, reliable,
available, and operable
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Guiding Roadmap Principles

Accelerating Bulk Updates Even Faster
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Incremental
Import
Rich
Capabilities
High performance
bulk load into
existing databases
Bulk updates are as easy as 1-2-3
1. Load updates in a separate database
(with the same performance
of a new load)
2. Prepare data for a merge
into an existing database
3. Merge updates into another database
…or execute all in one step
Differential data can be saved for multiple updates

Workspace: Unified Experience for Neo4j
Frictionless
Experience
Unified
Workspace
A single workspace
to unify the
experience of all
developer tools
42 EAP Sign-up! https://neo4j.com/workspace

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New GraphQL UI: Coming in 2H 2022
GraphQL UI
Frictionless
Experience
Low-code UI for
prototyping and
exploring the
GraphQL schema
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Autonomous Clustering in Neo4j 5
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Maximum scalability and on-demand infrastructure
Databases with independent functionality for availability and performance
Always online, with rolling upgrades and Fabric integration
Accessible from anywhere, in VPCs as well as publicly exposed when needed
Neo4j 5 Cluster
Enterprise
Grade
Flexible
Clustering
Fabric, parallel
implementations,
performance proof
points

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Other
Data Stores
Transactions Analytics
Graph Database Graph Data Science
Integrated AI/Machine
Learning
Data
Integrations
&
Connectors
Admin
Cypher
Drivers
&
APIs
Dev
Tools
Application Layer: Digital Twin, Recommendation, Fraud Detection, Cybersecurity, …
Query
Browser
GraphQL
Analytics & AI/Machine Learning Pipelines
The Neo4j Graph Data Platform
Flexible Graph Schema
Performance, Reliability &
Integrity
Scale-Up & Scale-Out Architecture
Development Tools Breadth
Enterprise Ecosystem

Amsterdam - The Neo4j Graph Data Platform Today & Tomorrow

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