Neo4j – The Fastest Path to Scalable Real-Time Analytics

Neo4j, Inc. All rights reserved 2021
Neo4j: The Fastest Path to
Scalable Real-Time Analytics
Dr. Jim Webber, Chief Scientist
Neo4j

2
175 ZB
Data Generated by 2025
(IDC)
55%
Becomes dark, untapped
data
Data is growing by 3X
every year, and
analyzing any data in
place and in real time is
getting highly complex.
2-3x
Growth per year
Over 2.5 Exabytes
Per Day

Tapping into this Data
Effectively Enables Real-Time
Analytics at Massive Scale
But Real-Time Analytics isn’t
Easy:
● Data is often stored stripped of its
relationship context, to ﬁnd its deeper value
and meaning
● Legacy technologies complicate the
analysis process
● Rigid schema in databases
● Terabytes, Petabytes of data analysis
needs millisecond response
● Rapid advancements in data modeling
require greater nimbleness and data
ﬂexibility

4
4
Rethinking How Data is Harnessed
Today’s biggest advancements and
innovations are being driven by
discovering, and understanding the
deeper relationships and connections
in data, in real-time and at scale...
Fraud Detection
Logistics
Cancer Research
Customer 360
Supply Chain
Health Informatics
Money Laundering
IT Operations

5
Data-Accelerated Real-Time Analytics
Activate
Scale
Unlock
with AI and machine learning
Connections inside your data
Big and cloud-ﬁrst developer agility

Activate
Scale
Unlock
Outcome- Build Intelligent
Applications Faster

Connected Data: The Foundation for Disruptive Evolution

Connected Data: Elevates Use Case Effectiveness
The plateau of aggregate data
Use
Case
Effectiveness
(value)
Use Case Maturity (time)
Connected data potential
Your opportunity
with Graph
Databases and
Algorithms

9
Graph Keeps Data in its Natural, Connected State
2010+
NoSQL Database
Scale-out, simple model store/retrieve
Optimized for application-driven, volume throughput
Relational Database
Structured, schema-based store/retrieve
Optimized for real-time transactional processing/reporting
1980+
Graph Database
Flexible, scale-out store/retrieve + analytics
Optimized for data connections, real-time transactions and analytics
2017+
First ISO standard database
language in 40 years.
Don’t handle relationships well
Practically limited to three degrees of separation
Don’t handle relationships at all
No language or structures for handling relationships:
joins done through the application
Natively store & query relationships
Queries run 1000x faster at scale, up to 1000+ hops
GQL
App
Based
SQL

10
Value in Data Relationships

Post
Profile Post
Post
Post
User Name
People Places Topics
Awards Activity
Calendar
Popular
Topics
Person Forum
Post
Tag
City,
Country
HAS_CREATOR
IS_ LOCATED_IN CONTAINER_OF
HAS_TAG
Evolving the Schema is EASY
“Flexible” Data Model
Easier to understand, evolve and collaborate

Value in Relationships has Created a Graph DBMS Market
Popularity Changes
© 2021, DB-Engines.com
12
400
600
800
1000
1200

“Using graph techniques at scale, will form
the foundation of modern data and
analytics.”
13
Top 10 Trends in Data and Analytics, 2020

14
Graph Databases Unlock Data-Context for
Richer Insights, Better Decisions, and Faster Innovation
Shifting from Data-Driven to Intelligence-Driven
Bought
B
ou
gh
t
V
i
e
w
e
d
R
e
t
u
r
n
e
d
Bought
K
n
o
w
s
Knows
Knows
K
n
o
w
s
Pl
ay
s
Lives_in
In_sport
Likes
F
a
n
_
o
f
Plays_for
People
E.g., Employees, Customers,
Suppliers, Partners, Inﬂuencers
Transactions
E.g., Risk management, Supply chain,
Payments
Knowledge
E.g. Enterprise content, Knowledgebase,
eCommerce content

Graphs Fueling Discovery and Transformation
in Every Field
Decision Analysis Customer Experience Data Unification Personalization Discovery
Fraud Prevention
Network Analysis
Forecasting
Patient/Customer Journey
Behavior Prediction
Data Disambiguation
Operations Optimization
Customers/Data 360
Compliance
Product Recommendations
Media and Advertising
Personalized Health
Drug Discovery
Product/Process Innovation
Intelligence & Security

16
The Forrester Wave™
for Graph Data Platforms, Q4 2020.
The Leading Graph Platform for Today’s Intelligent
Applications
“Neo4j is the clear market
leader in the graph space.
It has the most users, it uses
and drives a widely
adopted query language. In
many respects, it has
consistently been a lot more
innovative than its
competitors.”
June, 2020 - Bloor InBrief
#1 Most Popular Graph Database
with Developers
200k+
Developers

Blueprint: Building a Graph Powered Application for
Real-time Analytics
Neo4j GraphQL Library
React/ Angular/ Vue/ Svelte
Front-end development
Query/
Visualize/
Debug
Neo4j Browser
Neo4j Bloom
Neo4j Desktop
Data tools
Tableau/
Looker/
TIBCO Spotfire
Neo4j
BI-Connector
Business Intelligence
Dev/ Test/ Production
Neo4j Aura
Deployment

Activate
Scale
Unlock
Outcome- Get deeper, actionable
insights

“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

20
20
Top 10 Tech Trends in Data and Analytics, 16 Feb 2021
According to Gartner, “Graphs form the
foundation of modern D&A, with
capabilities to enhance and improve user
collaboration, ML models and explainable
AI.
The recent Gartner AI in Organizations
Survey demonstrates that graph
techniques are increasingly prevalent
as AI maturity grows, going from 13%
adoption when AI maturity is lowest to
48% when maturity is highest.”
AI Research Papers
Featuring Graph
Source: Dimensions Knowledge System
4x
Increase in
trafﬁc to
Neo4j GDS
page in
2H-2020
Analytics & Data Science Interest
Exploding in Neo4j Community
100k+
Practicing data
scientists engaged
with Neo4j
+210k
downloads

Graph
Application
Development
Graph Data
Science

Queries
Find the patterns you know exist
Machine Learning
Uncover trends and make
predictions
Visualization
Explore, collaborate, and explain
Graphs & Data Science
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Queries
Machine Learning Visualization

Person Forum
Post
Comment
Tag TagClass
City
Country
HAS_CREATOR
LIKES
IS_ LOCATED_IN
REPLY_OF
CONTAINER_OF
Find your friends
Help users discover
people they know or
might like to know
Community Detection, Similarity
Discover what's
buzzing
Tune in to global,
community or topical
trends.
Centrality
Driving engagement
Nudge users along
paths to success
Path Finding, Graph Embeddings

Community
Detection
Neo4j’s Graph Data Science Library
Unsupervised Graph Algorithms
Clustering
Dimension Reduction
(generalization)
Association
Which parts of my graph are
connected to each other?
Which nodes are
most similar?
How important is each node?
Supervised Machine Learning
Node Classiﬁcation
Link Prediction
Where will connections
form next?
What’s the label
for this node?
Centrality
Embeddings
Similarity
Pathﬁnding
More Algos than
any other vendor
ONLY in neo4j

More, Better, Faster Graph Algorithms
Pathfinding &
Search
• Shortest Path
• Single-Source Shortest Path
• All Pairs Shortest Path
• A* Shortest Path
• Yen’s K Shortest Path
• Minimum Weight Spanning Tree
• K-Spanning Tree (MST)
• Random Walk
• Breadth & Depth First Search
Centrality &
Importance
• Degree Centrality
• Closeness Centrality
• Harmonic Centrality
• Betweenness Centrality & Approx.
• PageRank
• Personalized PageRank
• ArticleRank
• Eigenvector Centrality
• Hyperlink Induced Topic Search (HITS)
• Influence Maximization (Greedy, CELF)
Community
Detection
• Triangle Count
• Local Clustering Coefficient
• Connected Components (Union Find)
• Strongly Connected Components
• Label Propagation
• Louvain Modularity
• K-1 Coloring
• Modularity Optimization
• Speaker Listener Label Propagation
• Approximate Max K-Cut
Heuristic Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocations
• Same Community
• Total Neighbors
Similarity
• Node Similarity
• K-Nearest Neighbors (KNN)
• Jaccard Similarity
• Cosine Similarity
• Pearson Similarity
• Euclidean Distance
• Approximate Nearest Neighbors (ANN)
Graph
Embeddings
• Node2Vec
• FastRP
• FastRPExtended
• GraphSAGE

Blueprint: Leveraging Graph Data Science For Deeper,
Actionable Insights
Discover patterns/
Predict growth/
Enrich data
Neo4j GDS
Deep business
insight
Visualize Data & Models
Neo4j Bloom
Explore and explain
Connect
Data Integration
Neo4j Connector
for Kafka/
for Spark

Activate
Scale
Unlock
Big and cloud-first developer agility
Outcome- Scale limitless with no data
compromise in real-time

Plan for Scale
TINKERING
1
THE BLADE YEARS
2
THE GROWTH
INFLECTION POINT
3
SURGING
GROWTH
4
REVENUE
START

F
A
H
R
Z
G
R
L
Q
M
S
W
T
N
B
Sharding in Other Databases
Shard 1 Shard 2 Shard 3
A
GET A
GET

30
Approach #1: Consistent Hashing
A
B
Shard 1 Shard 2 Shard 3
MATCH A B
MATCH A B

Approach #2: Graph Native Sharding
Size
Unbounded Size
Write
Concurrent Writes
Read
Constant Read Performance
Invest in
Data Modeling
Get These Magical
Properties:

Graphs of Activity & Behavior
10 Billion → Trillions
Graphs of Transactions
100 Million → 100 Billion
Graphs of
Things
1M → 1B
Graph Sizes ⇒ Scaling Strategy
Assets
Actions
Subjects
Replicated Clusters
Large Replicated Clusters / Scale-Out
Scale-Out & Sharding
KEY to Scaling Strategy

Neo4j: Run Anywhere, Everywhere at Scale
Self-Hosted Managed Services
Neo4j as a Fully-Managed Cloud-Native Service
Self-serve
⇅
Sales-assisted
Direct
⇅
Cloud Marketplace
Multi-cloud
⇅
Any Region
Workstation Data Center Private Cloud Hybrid Cloud Public Cloud Managed Service
Customer’s public,
private or hybrid cloud
environment

34
A Free Forever
Tier of Neo4j Aura
Sign up now at
neo4j.com/aura
Free

35
Data-Accelerated Real-Time Analytics at Scale
Activate
Scale
Unlock
Big and cloud-first developer agility
Outcome- Build Intelligent
Applications Faster
Outcome- Get deeper, actionable
insights
Outcome- Scale limitless with no data compromise in real-time

37
Thank you!
Sign up now at
neo4j.com/aura
Free Tier, Free Forever!

Neo4j – The Fastest Path to Scalable Real-Time Analytics

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