Neo4j GraphSummit Copenhagen - The path to success with Graph Database and Graph Data Science by Håkan Löfqvist.pptx

Neo4j, Inc. All rights reserved 2023
The path to success with Graph
Database and Graph Data Science
Håkan Löfqvist
Field Engineer

2
At the heart of every enterprise challenge
is an explosion of data complexity

3
{
"item": 67045,
"action": "VIEWED",
"visitor": 158090,
"timestamp": 1433222276276
}
Events
{
"item": 67045,
"action": "BOUGHT",
"visitor": 158090,
"transactionid": 11220,
"timestamp": 1433193915008
}
“Real world”

4
{
"item": 67045,
"action": "VIEWED",
"visitor": 158090,
"timestamp": 1433222276276
}
Events
{
"item": 67045,
"action": "BOUGHT",
"visitor": 158090,
"transactionid": 11220,
"timestamp": 1433193915008
}
visitor action item ts tx
1 1 4231 1433222276276 null
1 3 4231 1433193915008 3223
Events Table

5
id visitor_
fk
action_
fk
item_fk ts
1 1 1 4231 1433222276276
2 1 3 4231 1433193915008
Events Table
id action
1 VIEWED
2 ADDED
Actions
id visitor
1 158090
2 273216
Visitors
id …
Items
id …
Transaction Events

6
How do we make more money?
(We should be data driven)

7
😳

8

9
At the heart of every enterprise challenge
is a gap between the real world and data

10
Maybe Sven and Mica should
buy the Clear case too?

Graph Transactions,
Storage & Querying
Graph Analytics, ML,
& Data Science
Intelligent Applications Better Predictions
11
NEO4J: FOR APPLICATIONS AND ANALYTICS

12
NEO4J: FOR APPLICATIONS
const movies = await driver
.executeQuery(
'match (m:Movie) return m{.*} as movie limit toInteger($limit)',{limit: 10})
.then((result) => { return result.records.map( (r) => r.get('movie') ) })
.catch((error) => { console.error(error) })

13
PATTERN MATCHING IMPROVEMENTS
Simpler alternative syntax to navigate and
traverse your graph
- Disjunction (v:L1|L2)
- Conjunction (v:L1&L2)
- Negation (v:!L1)
- Grouping
(v:(L1&L2)|(L3&!L4))

14
INLINE PREDICATES
match (a:Actor where a.born > 1977)
match (a:Actor)-[r:ACTED_IN where 'Neo' in r.roles]->(m:Movie)

15
NEO4J FOR ANALYTICS
from graphdatascience import GraphDataScience
gds = GraphDataScience(DB_ULR, auth=(DB_USER, DB_PASS))
gds.louvain.write(G,writeProperty='community')

16
APACHE ARROWS
gds.alpha.graph.construct(
'items_graph',
nodes,
relationships
)

17
Pathfinding
& Search
Centrality Community
Detection
Machine
Learning
Link
Prediction
Similarity Embeddings And more …
Over 65 pretuned, parallelized algorithms. Iterate fast with different data sets, models,
and version trained models.
Bring the context of your connected data
into a format that other pipelines can
ingest.
The Largest Catalog of
Graph Algorithms
Native Graph Catalog and
Analytics Workspace
Graph Embeddings for
Machine Learning

18
Apache Spark
Connector
Data Warehouse
Connector
Apache Kafka
Connector
Neo4j BI
Connector

AUTONOMOUS CLUSTER
APP
Applications APP APP
Databases
Servers
3 primary + 2 secondary 3 primary
Composite Database
ML
Pipeline

20
AUTONOMOUS CLUSTER
A unified architecture to power all Neo4j
applications within an organization
Database and server administration
simplified (and with segregation of duties)
Move faster, scale out
Centralize operational tasks

21
FLEXIBLE DEPLOYMENT MODELS
Graph-as-a-Service Cloud Managed Services Self-hosted
Fully-managed SaaS
Consumption-based pricing
Cloud-native
Self-service deployment
No access to underlying
infrastructure and systems
White-glove managed service
by Neo4j experts
Fully customizable deployment
model and service levels
Operate In own data centers
or Virtual Private Cloud
For private and hybrid
cloud, or on-prem
Bring your own license
Full control of your environment
Run in any cloud, in your account

23
20 / 20
Top US banks
3 / 5
Top Aircraft Manufacturers
7 / 10
Top Telcos
3 / 5
Top Hotel Groups
8 / 10
Top Insurance Companies
10 /10
Top Automakers
7 / 10
Top Retailers
5 / 5
Top Pharmaceuticals
300
1B+ Enterprise
customers
$500M
in funding
170+
Global partner
ecosystem
250K
Community of developers
and data pros
100M+
Downloads

24
(we)-[:value]->(relationships)
together 🤗 we make sense of data

25
USER EXPERIENCE RESEARCH
● Participate in surveys and interviews
● Get the first sneak peek into new products and
features
● Directly influence our front-end products and
features
● Get compensation for your time (around $100
per hour)

26
Thank you!

Neo4j GraphSummit Copenhagen - The path to success with Graph Database and Graph Data Science by Håkan Löfqvist.pptx

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Neo4j GraphSummit Copenhagen - The path to success with Graph Database and Graph Data Science by Håkan Löfqvist.pptx