21. Let’s Hear a Few Stories
— David Meza, Chief Knowledge Architect at
NASA
“Neo4j saved well over two years
of work and one million dollars of
taxpayer funds.”
Impact
47. The Largest Graph Innovation Network
3,000,000+ with 50k additional per month
Neo4j Downloads
225+ customers
50% from Global 2000
100+
Technology and Services Partners
450+ annual events & 10k attendees
Graph and Neo4j awareness and training
43,000+
Neo4j Meetup Members
50,000+
Online and Classroom Education Registrants
49. RDBMS Vocabulary Mapped to Graph Modeling
Relational DB Construct Graph DB Construct
Entity table Node labels
Row Node
Columns Node properties
Technical primary keys Replace with business primary keys
Constraints Unique constraints for business keys
Indexes Indexes on any property
Foreign keys Relationships
Default values Not required
De-normalized or duplicated data Create separate nodes
Join tables Relationships
Join table columns Relationship properties
50. Good for discrete problems
Insufficient for connected
problems
RDBMS
53. At Write Time:
data is connected
as it is stored
At Read Time:
Lightning-fast retrieval of data and relationships via
pointer chasing
Index free adjacency
Graph Optimized Memory & Storage
56. 56
Example HR Query in SQL The Same Query using Cypher
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Project Impact
Less time writing queries
• More time understanding the answers
• Leaving time to ask the next question
Less time debugging queries:
• More time writing the next piece of code
• Improved quality of overall code base
Code that’s easier to read:
• Faster ramp-up for new project members
• Improved maintainability & troubleshooting
Productivity Gains with Graph Query Language
The query asks: “Find all direct reports and how many people they manage, up to three levels down”
57. Connectedness and Size of Data Set
ResponseTime
Relational and Other
NoSQL Databases
0 to 2 hops
0 to 3 degrees
Thousands of connections
1000x
Advantage
Tens to hundreds of hops
Thousands of degrees
Billions of connections
Graph
“Minutes to
milliseconds”
“Minutes to Milliseconds” Real-Time Query Performance
61. NoSQL Databases Don’t Handle Relationships
• No data structures to model or store
relationships
• No query constructs to support data
relationships
• Relating data requires “JOIN logic”
in the application
• No ACID support for transactions
… making NoSQL databases inappropriate when
data relationships are valuable in real-time
62. Data lake
Good for Analytics, BI, Map Reduce
Non-Operational, Slow Queries
RDBMS
74. Shopping Recommendations
Examples of companies that use Neo4j, the world’s leading graph database, for
recommendation and personalization engines.
Adidas uses Neo4j to combine
content and product data into a
single, searchable graph database
which is used to create a
personalized customer experience
“We have many different silos, many
different data domains, and in order to
make sense out of our data, we needed
to bring those together and make them
useful for us,”
– Sokratis Kartelias, Adidas
eBay ShopBot Personal Shopping
Companion in FB Messenger
“ShopBot uses its Knowledge Graph to
understand user requests and generate
follow-up questions to refine requests
before searching for the items in eBay’s
inventory. In a search query for “bags”
for example, purple nodes represent
“categories,” green “attributes” and
pink are “values” for those attributes.”
– RJ Pittman Blog, eBay
Walmart uses Neo4j to give
customer best web experience
through relevant and personal
recommendations
“As the current market leader in graph
databases, and with enterprise features
for scalability and availability, Neo4j is
the right choice to meet our demands”.
- Marcos Vada, Walmart
Product recommendations Personalization
Linkedin Chitu seeks to engage
Chinese jobseekers through a
game-like user interface that is
available on both desktop and
mobile devices.
“The challenge was speed,” said
Dong Bin, Manager of Development
at Chitu. “Due to the rate of growth
we saw from our competitors in the
Chinese market, we knew that we
had to launch Chitu as quickly as
possible.”
Social Network
Additional Case Studies