Download as PDF, PPTX
![(Michael) -[:WORKS_ON]-> (Neo4j)
ME
Spring Cloud
Community
Cypher
console
community
graph
Server
2](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-2-320.jpg)
![// lookup starting point in an index
START n=node:People(name = ‘Andreas’)
Andreas
You traverse the graph
21
// then traverse to find results
START me=node:People(name = ‘Andreas’
MATCH (me)-[:FRIEND]-(friend)-[:FRIEND]-(friend2)
RETURN friend2](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-54-320.jpg)
![SELECT skills.*, user_skill.*
FROM users
JOIN user_skill ON users.id = user_skill.user_id
JOIN skills ON user_skill.skill_id = skill.id WHERE users.id = 1
22
START user = node(1)
MATCH user -[user_skill]-> skill
RETURN skill, user_skill](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-56-320.jpg)
![Anti-pattern: rich properties
name: “Canada”
languages_spoken: “[ ‘English’, ‘French’ ]”](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-77-320.jpg)
![63
Real World Use Cases:
•[A] ACL from Hell](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-99-320.jpg)
![63
Real World Use Cases:
•[A] ACL from Hell
•[B] Timely recommendations](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-100-320.jpg)
![63
Real World Use Cases:
•[A] ACL from Hell
•[B] Timely recommendations
•[C] Global collaboration](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-101-320.jpg)
![[A] ACL from Hell
64](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-102-320.jpg)
![[A] ACL from Hell
๏ Customer:
• leading consumer utility company with tons and
tons of users
๏ Goal:
• comprehensive access control administration
for customers
๏ Benefits:
• Flexible and dynamic architecture
• Exceptional performance
• Extensible data model supports new
applications and features
• Low cost
64](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-103-320.jpg)
![[A] ACL from Hell
๏ Customer:
• leading consumer utility company with tons and
tons of users
๏ Goal:
• comprehensive access control administration
for customers
๏ Benefits:
• Flexible and dynamic architecture
• Exceptional performance
• Extensible data model supports new
applications and features
• Low cost
64
• A Reliable access control administration system for
5 million customers, subscriptions and agreements
• Complex dependencies between groups, companies,
individuals, accounts, products, subscriptions, services and
agreements
• Broad and deep graphs (master customers with 1000s of
customers, subscriptions & agreements)](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-104-320.jpg)
![[A] ACL from Hell
๏ Customer:
• leading consumer utility company with tons and
tons of users
๏ Goal:
• comprehensive access control administration
for customers
๏ Benefits:
• Flexible and dynamic architecture
• Exceptional performance
• Extensible data model supports new
applications and features
• Low cost
64
• A Reliable access control administration system for
5 million customers, subscriptions and agreements
• Complex dependencies between groups, companies,
individuals, accounts, products, subscriptions, services and
agreements
• Broad and deep graphs (master customers with 1000s of
customers, subscriptions & agreements)
name: Andreas
subscription: sports
service: NFL
account: 9758352794
agreement: ultimate
owns
subscribes to
has plan
includes
provides group: graphistas
promotion: fall
member of
offered
discounts
company: Neo
Technologyworks with
gets discount on
subscription: local
subscribes to
provides service: Ravens
includes](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-105-320.jpg)
![[B] Timely Recommendations
65](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-106-320.jpg)
![[B] Timely Recommendations
๏ Customer:
• a professional social network
• 35 millions users, adding 30,000+ each day
๏ Goal: up-to-date recommendations
• Scalable solution with real-time end-user
experience
• Low maintenance and reliable architecture
• 8-week implementation
65](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-107-320.jpg)
![[B] Timely Recommendations
๏ Customer:
• a professional social network
• 35 millions users, adding 30,000+ each day
๏ Goal: up-to-date recommendations
• Scalable solution with real-time end-user
experience
• Low maintenance and reliable architecture
• 8-week implementation
65
๏ Problem:
• Real-time recommendation imperative to attract new
users and maintain positive user retention
• Clustered MySQL solution not scalable or fast enough
to support real-time requirements
๏ Upgrade from running a batch job
• initial hour-long batch job
• but then success happened, and it became a day
• then two days
๏ With Neo4j, real time recommendations](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-108-320.jpg)
![[B] Timely Recommendations
๏ Customer:
• a professional social network
• 35 millions users, adding 30,000+ each day
๏ Goal: up-to-date recommendations
• Scalable solution with real-time end-user
experience
• Low maintenance and reliable architecture
• 8-week implementation
65
๏ Problem:
• Real-time recommendation imperative to attract new
users and maintain positive user retention
• Clustered MySQL solution not scalable or fast enough
to support real-time requirements
๏ Upgrade from running a batch job
• initial hour-long batch job
• but then success happened, and it became a day
• then two days
๏ With Neo4j, real time recommendations
name:Andreas
job: talking
name: Allison
job: plumber
name: Tobias
job: coding
knows
knows
name: Peter
job: building
name: Emil
job: plumber
knows
name: Stephen
job: DJ
knows
knows
name: Delia
job: barking
knows
knows
name: Tiberius
job: dancer
knows
knows
knows
knows](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-109-320.jpg)
![[C] Collaboration on Global Scale
66](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-110-320.jpg)
![[C] Collaboration on Global Scale
๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
66](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-111-320.jpg)
![[C] Collaboration on Global Scale
๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
66
• Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
• Infer collaborative relationships through user-
generated content
• Worldwide Availability](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-112-320.jpg)
![[C] Collaboration on Global Scale
๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
66
• Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
• Infer collaborative relationships through user-
generated content
• Worldwide Availability
Asia North America Europe](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-113-320.jpg)
![[C] Collaboration on Global Scale
๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
66
• Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
• Infer collaborative relationships through user-
generated content
• Worldwide Availability
Asia North America Europe
Asia North America Europe](https://image.slidesharecdn.com/webinardatamodeling-130713191849-phpapp01/85/Data-Modeling-with-Neo4j-114-320.jpg)
Uploaded byNeo4j
Data Modeling with Neo4j
The document discusses data modeling using Neo4j, a NoSQL graph database, emphasizing the importance of relationships in graph structures compared to traditional relational databases. It covers concepts like property graphs, the connected data model, and real-world use cases across different applications. The document also provides guidance on getting started with Neo4j, including accessing documentation and tutorials.
More Related Content
Similar to Data Modeling with Neo4j
Data Modeling with Neo4j
- 1. Data Modeling with Neo4j 1 MichaelHunger, Neo Technology @neo4j | @mesirii | michael@neo4j.org
- 2. (Michael) -[:WORKS_ON]-> (Neo4j) ME SpringCloud Community Cypher console community graph Server 2
- 3.
- 4.
- 5.
- 6.
- 7.
- 8. A graph database... 7 NO:not for charts & diagrams, or vector artwork
- 9. A graph database... 7 NO:not for charts & diagrams, or vector artwork YES: for storing data that is structured as a graph
- 10. A graph database... 7 NO:not for charts & diagrams, or vector artwork YES: for storing data that is structured as a graph remember linked lists, trees?
- 11. A graph database... 7 NO:not for charts & diagrams, or vector artwork YES: for storing data that is structured as a graph remember linked lists, trees? graphs are the general-purpose data structure
- 12. A graph database... 7 NO:not for charts & diagrams, or vector artwork YES: for storing data that is structured as a graph remember linked lists, trees? graphs are the general-purpose data structure “A relational database may tell you the average age of everyone in this place, but a graph database will tell you who is most likely to buy you a beer.”
- 13.
- 14.
- 15.
- 16.
- 17.
- 18. You know relational 8 foobarfoo_bar
- 19. You know relational 8 foobarfoo_bar
- 20. You know relational 8 foobarfoo_bar
- 21. You know relational 8 foobarfoo_bar
- 22. You know relational 8 nowconsider relationships...
- 23. You know relational 8 nowconsider relationships...
- 24. You know relational 8 nowconsider relationships...
- 25. You know relational 8 nowconsider relationships...
- 26. You know relational 8 nowconsider relationships...
- 27. You know relational 8 nowconsider relationships...
- 28.
- 29.
- 30. We're talking abouta Property Graph 9
- 31. We're talking abouta Property Graph 9 Nodes
- 32. We're talking abouta Property Graph 9 Nodes Relationships
- 33. Emil Andrés Lars Johan Allison Peter Michael Tobias Andreas IanMica Delia knows knows knows knows knows knows knows knows knows knowsMica knowsknows Mica Delia knows We're talking abouta Property Graph 9 Nodes Relationships Properties (each a key+value)
- 34. Emil Andrés Lars Johan Allison Peter Michael Tobias Andreas IanMica Delia knows knows knows knows knows knows knows knows knows knowsMica knowsknows Mica Delia knows We're talking abouta Property Graph 9 Nodes Relationships Properties (each a key+value) + Indexes (for easy look-ups)
- 35.
- 36.
- 37. 12 “There is asignificant downside - the whole approach works really well when data access is aligned with the aggregates, but what if you want to look at the data in a different way? Order entry naturally stores orders as aggregates, but analyzing product sales cuts across the aggregate structure. The advantage of not using an aggregate structure in the database is that it allows you to slice and dice your data different ways for different audiences. This is why aggregate-oriented stores talk so much about map- reduce.” Martin Fowler Aggregate Oriented Model
- 38. 13 The connected datamodel is based on fine grained elements that are richly connected, the emphasis is on extracting many dimensions and attributes as elements. Connections are cheap and can be used not only for the domain-level relationships but also for additional structures that allow efficient access for different use-cases. The fine grained model requires a external scope for mutating operations that ensures Atomicity, Consistency, Isolation and Durability - ACID also known as Transactions. Michael Hunger Connected Data Model
- 39.
- 40. Why Data Modeling 15 ๏Whatis modeling? ๏Aren‘t we schema free? ๏How does it work in a graph? ๏Where should modeling happen? DB or Application
- 41.
- 42.
- 43. Model mis-match Application ModelDatabase Model
- 44.
- 45.
- 46.
- 47.
- 48.
- 49. Whiteboard --> Data 20 Andreas Peter Emil Allison knows knowsknows knows // Cypher query - friend of a friend start n=node(0) match (n)--()--(foaf) return foaf
- 50.
- 51. You traverse thegraph 21
- 52. You traverse thegraph 21
- 53. // lookup startingpoint in an index START n=node:People(name = ‘Andreas’) Andreas You traverse the graph 21
- 54. // lookup startingpoint in an index START n=node:People(name = ‘Andreas’) Andreas You traverse the graph 21 // then traverse to find results START me=node:People(name = ‘Andreas’ MATCH (me)-[:FRIEND]-(friend)-[:FRIEND]-(friend2) RETURN friend2
- 55.
- 56. SELECT skills.*, user_skill.* FROMusers JOIN user_skill ON users.id = user_skill.user_id JOIN skills ON user_skill.skill_id = skill.id WHERE users.id = 1 22 START user = node(1) MATCH user -[user_skill]-> skill RETURN skill, user_skill
- 57.
- 58. What language dothey speak here? Language Country
- 59. What language dothey speak here? Language Country
- 60. What language dothey speak here? Language Country
- 61.
- 62. Need to modelthe relationship language_code language_name word_count Language country_code country_name flag_uri language_code Country
- 63. What if thecardinality changes? language_code language_name word_count country_code Language country_code country_name flag_uri Country
- 64. Or we gomany-to-many? language_code language_name word_count Language country_code country_name flag_uri Country language_code country_code LanguageCountry
- 65. Or we wantto qualify the relationship? language_code language_name word_count Language country_code country_name flag_uri Country language_code country_code primary LanguageCountry
- 66.
- 67.
- 68.
- 69.
- 70. What’s different? ๏ Implementationof maintaining relationships is left up to the database ๏ Artificial keys disappear or are unnecessary ๏ Relationships get an explicit name • can be navigated in both directions
- 71.
- 72.
- 73.
- 74. Keep on addingrelationships name word_count Language name flag_uri Country POPULATION_SPEAKS population_fraction SIMILAR_TO ADJACENT_TO
- 75.
- 76. Use the buildingblocks ๏ Nodes ๏ Relationships ๏ Properties name: value RELATIONSHIP_NAME
- 77. Anti-pattern: rich properties name:“Canada” languages_spoken: “[ ‘English’, ‘French’ ]”
- 78.
- 79. Anti-Pattern: Node representsmultiple concepts name flag_uri language_name number_of_words yes_in_language no_in_language currency_code currency_name Country
- 80. USES_CURRENCY Split up inseparate concepts name flag_uri currency_code currency_name Country name number_of_words yes no Country SPEAKS Currency currency_code currency_name
- 81. Challenge: Property orRelationship? ๏ Can every property be replaced by a relationship? • Hint: triple stores. Are they easy to use? ๏ Should every entities with the same property values be connected?
- 82. Object Mapping ๏ Similarto how you would map objects to a relational database, using an ORM such as Hibernate ๏ Generally simpler and easier to reason about ๏ Examples • Java: Spring Data Neo4j • Ruby: Active Model ๏ Why Map? • Do you use mapping because you are scared of SQL? • Following DDD, could you write your repositories directly against the graph API?
- 83. CONNECT for fastaccess In-Graph Indices
- 84. Relationships for querying ๏like in other databases • same structure for different use-cases (OLTP and OLAP) doesn‘t work • graph allows: add more structures ๏ Relationships should the primary means to access nodes in the database ๏ Traversing relationships is cheap – that’s the whole design goal of a graph database ๏ Use lookups only to find starting nodes for a query Data Modeling examples in Manual
- 85. Anti-pattern: unconnected graph name:“Jones” name: “Jones” name: “Jones” name: “Jones” name: “Jones” name: “Jones” name: “Jones” name: “Jones” name: “Jones” name: “Jones” name: “Jones”
- 86.
- 87.
- 88.
- 89.
- 90.
- 91.
- 92.
- 93. Evolution: Relationship toNode 59 Peter SENT_EMAIL Michael Peter EMAIL_FROM Michael EMAIL_TO Email Emil EMAIL_CC Community TAGGED . . . see Hyperedges
- 94. Combine multiple Domainsin a Graph ๏ you start with a single domain ๏ add more connected domains as your system evolves ๏ more domains allow to ask different queries ๏ one domain „indexes“ the other ๏ Example Facebook Graph Search • social graph • location graph • activity graph • favorite graph • ...
- 95. Notes on theGraph Data Model ๏Schema free, but constraints ๏Model your graph with a whiteboard and a wise man ๏Nodes as main entities but useless without connections ๏Relationships are first level citizens in the model and database ๏Normalize more than in a relational database ๏use meaningful relationship-types, not generic ones like IS_ ๏use in-graph structures to allow different access paths ๏evolve your graph to your needs, incremental growth 61
- 96.
- 97.
- 98. 63 Real World UseCases:
- 99. 63 Real World UseCases: •[A] ACL from Hell
- 100. 63 Real World UseCases: •[A] ACL from Hell •[B] Timely recommendations
- 101. 63 Real World UseCases: •[A] ACL from Hell •[B] Timely recommendations •[C] Global collaboration
- 102. [A] ACL fromHell 64
- 103. [A] ACL fromHell ๏ Customer: • leading consumer utility company with tons and tons of users ๏ Goal: • comprehensive access control administration for customers ๏ Benefits: • Flexible and dynamic architecture • Exceptional performance • Extensible data model supports new applications and features • Low cost 64
- 104. [A] ACL fromHell ๏ Customer: • leading consumer utility company with tons and tons of users ๏ Goal: • comprehensive access control administration for customers ๏ Benefits: • Flexible and dynamic architecture • Exceptional performance • Extensible data model supports new applications and features • Low cost 64 • A Reliable access control administration system for 5 million customers, subscriptions and agreements • Complex dependencies between groups, companies, individuals, accounts, products, subscriptions, services and agreements • Broad and deep graphs (master customers with 1000s of customers, subscriptions & agreements)
- 105. [A] ACL fromHell ๏ Customer: • leading consumer utility company with tons and tons of users ๏ Goal: • comprehensive access control administration for customers ๏ Benefits: • Flexible and dynamic architecture • Exceptional performance • Extensible data model supports new applications and features • Low cost 64 • A Reliable access control administration system for 5 million customers, subscriptions and agreements • Complex dependencies between groups, companies, individuals, accounts, products, subscriptions, services and agreements • Broad and deep graphs (master customers with 1000s of customers, subscriptions & agreements) name: Andreas subscription: sports service: NFL account: 9758352794 agreement: ultimate owns subscribes to has plan includes provides group: graphistas promotion: fall member of offered discounts company: Neo Technologyworks with gets discount on subscription: local subscribes to provides service: Ravens includes
- 106.
- 107. [B] Timely Recommendations ๏Customer: • a professional social network • 35 millions users, adding 30,000+ each day ๏ Goal: up-to-date recommendations • Scalable solution with real-time end-user experience • Low maintenance and reliable architecture • 8-week implementation 65
- 108. [B] Timely Recommendations ๏Customer: • a professional social network • 35 millions users, adding 30,000+ each day ๏ Goal: up-to-date recommendations • Scalable solution with real-time end-user experience • Low maintenance and reliable architecture • 8-week implementation 65 ๏ Problem: • Real-time recommendation imperative to attract new users and maintain positive user retention • Clustered MySQL solution not scalable or fast enough to support real-time requirements ๏ Upgrade from running a batch job • initial hour-long batch job • but then success happened, and it became a day • then two days ๏ With Neo4j, real time recommendations
- 109. [B] Timely Recommendations ๏Customer: • a professional social network • 35 millions users, adding 30,000+ each day ๏ Goal: up-to-date recommendations • Scalable solution with real-time end-user experience • Low maintenance and reliable architecture • 8-week implementation 65 ๏ Problem: • Real-time recommendation imperative to attract new users and maintain positive user retention • Clustered MySQL solution not scalable or fast enough to support real-time requirements ๏ Upgrade from running a batch job • initial hour-long batch job • but then success happened, and it became a day • then two days ๏ With Neo4j, real time recommendations name:Andreas job: talking name: Allison job: plumber name: Tobias job: coding knows knows name: Peter job: building name: Emil job: plumber knows name: Stephen job: DJ knows knows name: Delia job: barking knows knows name: Tiberius job: dancer knows knows knows knows
- 110. [C] Collaboration onGlobal Scale 66
- 111. [C] Collaboration onGlobal Scale ๏ Customer: a worldwide software leader • highly collaborative end-users ๏ Goal: offer an online platform for global collaboration • Highly flexible data analysis • Sub-second results for large, densely-connected data • User experience - competitive advantage 66
- 112. [C] Collaboration onGlobal Scale ๏ Customer: a worldwide software leader • highly collaborative end-users ๏ Goal: offer an online platform for global collaboration • Highly flexible data analysis • Sub-second results for large, densely-connected data • User experience - competitive advantage 66 • Massive amounts of data tied to members, user groups, member content, etc. all interconnected • Infer collaborative relationships through user- generated content • Worldwide Availability
- 113. [C] Collaboration onGlobal Scale ๏ Customer: a worldwide software leader • highly collaborative end-users ๏ Goal: offer an online platform for global collaboration • Highly flexible data analysis • Sub-second results for large, densely-connected data • User experience - competitive advantage 66 • Massive amounts of data tied to members, user groups, member content, etc. all interconnected • Infer collaborative relationships through user- generated content • Worldwide Availability Asia North America Europe
- 114. [C] Collaboration onGlobal Scale ๏ Customer: a worldwide software leader • highly collaborative end-users ๏ Goal: offer an online platform for global collaboration • Highly flexible data analysis • Sub-second results for large, densely-connected data • User experience - competitive advantage 66 • Massive amounts of data tied to members, user groups, member content, etc. all interconnected • Infer collaborative relationships through user- generated content • Worldwide Availability Asia North America Europe Asia North America Europe
- 115. How to getstarted? 67
- 116. How to getstarted? ๏ Documentation 67
- 117. How to getstarted? ๏ Documentation • neo4j.org 67
- 118. How to getstarted? ๏ Documentation • neo4j.org 67
- 119. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql 67
- 120. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql 67
- 121. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference 67
- 122. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference 67
- 123. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples 67
- 124. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples • http://console.neo4j.org 67
- 125. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples • http://console.neo4j.org 67
- 126. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples • http://console.neo4j.org • Neo4j in Action 67
- 127. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples • http://console.neo4j.org • Neo4j in Action 67
- 128. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples • http://console.neo4j.org • Neo4j in Action • Good Relationships 67
- 129. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples • http://console.neo4j.org • Neo4j in Action • Good Relationships ๏ Worldwide one-day Neo4j Trainings 67
- 130. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples • http://console.neo4j.org • Neo4j in Action • Good Relationships ๏ Worldwide one-day Neo4j Trainings ๏ Get Neo4j 67
- 131. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples • http://console.neo4j.org • Neo4j in Action • Good Relationships ๏ Worldwide one-day Neo4j Trainings ๏ Get Neo4j • http://neo4j.org/download 67
- 132. How to getstarted? ๏ Documentation • neo4j.org ‣http://www.neo4j.org/learn/nosql • docs.neo4j.org - tutorials+reference ‣Data Modeling Examples • http://console.neo4j.org • Neo4j in Action • Good Relationships ๏ Worldwide one-day Neo4j Trainings ๏ Get Neo4j • http://neo4j.org/download • http://addons.heroku.com/neo4j/ 67
- 133.
- 134. 68 Really, once youstart thinking in graphs it's hard to stop Recommendations MDM Systems Management Geospatial Social computing Business intelligence Biotechnology Making Sense of all that data your brain access control linguistics catalogs genealogyrouting compensation market vectors
- 135. 68 Really, once youstart thinking in graphs it's hard to stop Recommendations MDM Systems Management Geospatial Social computing Business intelligence Biotechnology Making Sense of all that data your brain access control linguistics catalogs genealogyrouting compensation market vectors What will you build?
- 136.