SlideShare a Scribd company logo
1 of 38
Cédric Fauvet
Cedric.fauvet@neotechnology.com
Twitter Francophone: @Neo4jFr
1
Confidential - Neo Technology, Inc.
New opportunities for connected data :
Neo4j the graph database
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’s « SQL »
The graph theory
An 840 : The horeseman’s problem
The Arab mathematician and chess master
al-Adli ar-Rumi solved the problem.
The graph theory
An 1735 The Königsberg’s 7 bridges
problem
How to pass through the bridges only once ?
Leonhard Euler
Swiss mathematician
The graph theory
2013: Today’s questions
• Collaboration
• Configuration management
• Geo mapping
• Molecule’s Interaction (Biology)
• Impact analysis
• Master Data Management
• Product management
• Recommendation
• Social
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’s « SQL »
Neo Technology (Neo4j) Corporate Overview
• Neo4j founded 2000
• Headquartered in Palo Alto, California
• Engineering headquarter in Malmö, Sweden
• Employees based in France, Germany, UK, Sweden, US, and Malaysia
• 24/7 support on global basis
• 100,000+ users
• F500 customers such as Adobe, Cisco, Deutsche
Telecom, Telenor, Deutsche Post, SFR, Lockheed Martin, and others
• SI partners such as Accenture and dozens of local SI boutiques
• Technology partners such as VMware, Informatica and Microsoft
• Leader in the Graph Database arena
• Mission: Help the world to make sense of data
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’s « SQL »
Société
- Worldwide company
- 45 millions users, + 30 000 each days.
- Owner of the social networks
ApnaCircle (Inde) and Tianji (Chine)
Problème
Viadeo, integrated Neo4j as their backend database, to
store all of their users and relationships. When their
network expanded to a level that their traditional MySQL
database couldn’t handle, Viadeo experienced
performance and storage issues that would not perform
at the rate the
company was growing.
Etude de cas: Réseau social
Bénéfices & time frame
- Real time
recomendation with
Neo4j.
- Project timeframe
= 8 weeks
Solution
Integrating Neo4j, Viadeo has highly accelerated their
system in two ways. Neo4j increased Viadeo’s performance
by requiring less storage space andless time to restructure
the graph.
10
Company
- Worldwide leader in networking for the Internet
Solution
- Clustered Neo4j Enterprise architecture
- Part of a larger infrastructure solution
- Multi-region AWS deployment
- Neo4j selected in competition with custom solution
and Oracle
Benefits & time frame
- Highly flexible data analysis
- Sub-second results for large, densely-connected datasets
- User experience - competitive advantage
- 12 month project
Problem definition
- Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
- Need to infer collaborative relationships based on
user-generated content
Case study: Web/ISV - social collaboration
Adobe
11
Company
- Leading telco provider in the Nordics
Solution
- Neo4j Enterprise solution
- Embedded + HA
- Replacing 10 yr-old Oracle, Berkeley DB and a
mainframe environment
Problem definition
- Need: Reliable access control administration system
for 5mio customers, subscriptions and agreements
- Complex dependencies between
groups, companies, individuals, accounts, products, s
ubscriptions, services and agreements
- Broad and deep graphs (master customers with
1000s of customers, subscriptions & agreements)
Case study: Telco
Telenor
Benefits & time frame
- Flexible and dynamic architecture
- Exceptional performance
-Low cost compared to alternatives
-Extensible data model supports new applications and
features
12
Company
-World wide leader in network infrastructure
-Large sales organization
Solution
-2x Highly Available Neo4j clusters
-One live cluster and one backup / hot spare cluster
at a different datacenter
-Total: 6 Embedded Enterprise Neo4j DBs
Benefits & timeframe
-Real time overview of sales accounts and owners
-The ability to model complex rules for account ownership
-Direct commissioning computation through the entire sales
organization
->12 month development and rollout
Problem definition
-Intricate rules governing ownership of sales accounts
-Complex rules for sales commissions
-Queries complicated to structure with RDBMS
-Oracle performance not good enough for online
account management
Case study: Sales account management
Cisco
Use case – What’s in common ?
Alice
ACME
ACME
EMEA
Bob
Retail Co.
FooBar Inc.
Sales Rep
Sales Rep
Worked For
Worked For
Sold To
Use case – What’s a best path ?
Retail Co.
Bob
ACME
Steve
Jane
Liza
Pauline
William
Sales Rep
VP
CMO
Sales Rep
VP
Use case : Pattern matching
Fraud detection
Correspondance
Fraud detection
Pas de correspondance
Fraud detection
Graph navigation
Impact analysis
Start node
Impact analysis
Follow the relationships
Impact analysis
Evaluate each node
Impact analysis
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’s « SQL »
Trend 1:Exponential
growth of data
0
250
500
750
1000
2007 2008 2009 2010
Exabytes of new unique digital information
size * connectivity = complexity
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’s « SQL »
Neo4j
Tackles complex data:
– Large
– Densely-connected
– Semi-structured
Neo4j characteristics
• Fully ACID
– Including XA-compliant distributed two-phase commits
• High Availability / Read Scaling through master-slave
replication with master failover
• In-memory speeds with warm caches while
maintaining full ACID
• Cypher query language and Java APIs
Caractéristiques de Neo4j
• Transactions Full ACID
– XA-compliant distributed two-phase commits
• Haute disponibilité / Scalabilité*
– master-slave réplication avec master Fail-over
– * Lecture
• Hautes performance en mémoire
– Caches évolués full ACID
• Langage des requêtes
– Cypher
– Java APIs
– JDBC
– Rest API
– Ruby
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’s « SQL »
() --> ()
Cypher the Neo4j’s « SQL »
Based on ACSII-Art
(A) --> (B)
A B
Cypher the Neo4j’s « SQL »
Each node have a identifier
A -[:LOVES]-> B
LOVES
A B
Cypher the Neo4j’s « SQL »
Relationship
A --> B --> C
A B C
Cypher the Neo4j’s « SQL »
You can traverse the graph
A -[*]-> B
A B
A B
A B
Cypher the Neo4j’s « SQL »
You can dynamically traverse the graph
Cypher the Neo4j’s « SQL »
The friend of friend query
START john=node:node_auto_index(name = 'John')
MATCH john-[:friend]->()-[:friend]->fof
RETURN john, fof
Thank you
Let’s move forward together !
Cédric Fauvet Your contact in France and switzerland
E-mail : Cedric.fauvet@neotechnology.com
French speaking Twitter : @Neo4jFr
French speaking community : meetup.com/graphdb-france

More Related Content

What's hot

GoldenGate and Oracle Data Integrator - A Perfect Match- Upgrade to 12c
GoldenGate and Oracle Data Integrator - A Perfect Match- Upgrade to 12cGoldenGate and Oracle Data Integrator - A Perfect Match- Upgrade to 12c
GoldenGate and Oracle Data Integrator - A Perfect Match- Upgrade to 12cMichael Rainey
 
MySQL Document Store (Oracle Code Warsaw 2018)
MySQL Document Store (Oracle Code Warsaw 2018)MySQL Document Store (Oracle Code Warsaw 2018)
MySQL Document Store (Oracle Code Warsaw 2018)Vittorio Cioe
 
Offload, Transform, and Present - the New World of Data Integration
Offload, Transform, and Present - the New World of Data IntegrationOffload, Transform, and Present - the New World of Data Integration
Offload, Transform, and Present - the New World of Data IntegrationMichael Rainey
 
2018: State of the Dolphin, MySQL Keynote at Percona Live Europe 2018, Frankf...
2018: State of the Dolphin, MySQL Keynote at Percona Live Europe 2018, Frankf...2018: State of the Dolphin, MySQL Keynote at Percona Live Europe 2018, Frankf...
2018: State of the Dolphin, MySQL Keynote at Percona Live Europe 2018, Frankf...Geir Høydalsvik
 
ODI 11g in the Enterprise - BIWA 2013
ODI 11g in the Enterprise - BIWA 2013ODI 11g in the Enterprise - BIWA 2013
ODI 11g in the Enterprise - BIWA 2013Mark Rittman
 
MySQL 8.0, what's new ? - Forum PHP 2018
MySQL 8.0, what's new ? - Forum PHP 2018MySQL 8.0, what's new ? - Forum PHP 2018
MySQL 8.0, what's new ? - Forum PHP 2018Olivier DASINI
 
Pythian operational visibility
Pythian operational visibilityPythian operational visibility
Pythian operational visibilityLaine Campbell
 
GraphDay Paris - Intro & Découverte de l'outil de visualisation Neo4j Bloom
GraphDay Paris - Intro & Découverte de l'outil de visualisation Neo4j Bloom GraphDay Paris - Intro & Découverte de l'outil de visualisation Neo4j Bloom
GraphDay Paris - Intro & Découverte de l'outil de visualisation Neo4j Bloom Neo4j
 
IBANK - Oracle developers-guide
IBANK - Oracle developers-guide IBANK - Oracle developers-guide
IBANK - Oracle developers-guide ibankuk
 
GoldenGate and Oracle Data Integrator - A Perfect Match...
GoldenGate and Oracle Data Integrator - A Perfect Match...GoldenGate and Oracle Data Integrator - A Perfect Match...
GoldenGate and Oracle Data Integrator - A Perfect Match...Michael Rainey
 
DataOps - Lean principles and lean practices
DataOps - Lean principles and lean practicesDataOps - Lean principles and lean practices
DataOps - Lean principles and lean practicesLars Albertsson
 
GraphConnect Europe 2016 - Governing Multichannel Services with Graphs - Albe...
GraphConnect Europe 2016 - Governing Multichannel Services with Graphs - Albe...GraphConnect Europe 2016 - Governing Multichannel Services with Graphs - Albe...
GraphConnect Europe 2016 - Governing Multichannel Services with Graphs - Albe...Neo4j
 
Choosing the Right Open Source Database
Choosing the Right Open Source DatabaseChoosing the Right Open Source Database
Choosing the Right Open Source DatabaseAll Things Open
 
Pldc2012 monitoring-and-trending-with-mysql
Pldc2012 monitoring-and-trending-with-mysqlPldc2012 monitoring-and-trending-with-mysql
Pldc2012 monitoring-and-trending-with-mysqlradiocats
 
Oracle Autonomous Data Warehouse Cloud Webex - with Demo
Oracle Autonomous Data Warehouse Cloud Webex - with DemoOracle Autonomous Data Warehouse Cloud Webex - with Demo
Oracle Autonomous Data Warehouse Cloud Webex - with DemoIslam Gohar
 
GraphTour - Closing Keynote
GraphTour - Closing KeynoteGraphTour - Closing Keynote
GraphTour - Closing KeynoteNeo4j
 
Caveats of hosting MariaDB on Microsoft Azure Cloud
Caveats of hosting MariaDB on Microsoft Azure CloudCaveats of hosting MariaDB on Microsoft Azure Cloud
Caveats of hosting MariaDB on Microsoft Azure CloudMariaDB plc
 
M|18 How We Made the Move to MariaDB at FNI
M|18 How We Made the Move to MariaDB at FNIM|18 How We Made the Move to MariaDB at FNI
M|18 How We Made the Move to MariaDB at FNIMariaDB plc
 

What's hot (20)

GoldenGate and Oracle Data Integrator - A Perfect Match- Upgrade to 12c
GoldenGate and Oracle Data Integrator - A Perfect Match- Upgrade to 12cGoldenGate and Oracle Data Integrator - A Perfect Match- Upgrade to 12c
GoldenGate and Oracle Data Integrator - A Perfect Match- Upgrade to 12c
 
MySQL Document Store (Oracle Code Warsaw 2018)
MySQL Document Store (Oracle Code Warsaw 2018)MySQL Document Store (Oracle Code Warsaw 2018)
MySQL Document Store (Oracle Code Warsaw 2018)
 
Offload, Transform, and Present - the New World of Data Integration
Offload, Transform, and Present - the New World of Data IntegrationOffload, Transform, and Present - the New World of Data Integration
Offload, Transform, and Present - the New World of Data Integration
 
2018: State of the Dolphin, MySQL Keynote at Percona Live Europe 2018, Frankf...
2018: State of the Dolphin, MySQL Keynote at Percona Live Europe 2018, Frankf...2018: State of the Dolphin, MySQL Keynote at Percona Live Europe 2018, Frankf...
2018: State of the Dolphin, MySQL Keynote at Percona Live Europe 2018, Frankf...
 
ODI 11g in the Enterprise - BIWA 2013
ODI 11g in the Enterprise - BIWA 2013ODI 11g in the Enterprise - BIWA 2013
ODI 11g in the Enterprise - BIWA 2013
 
MySQL 8.0, what's new ? - Forum PHP 2018
MySQL 8.0, what's new ? - Forum PHP 2018MySQL 8.0, what's new ? - Forum PHP 2018
MySQL 8.0, what's new ? - Forum PHP 2018
 
Pythian operational visibility
Pythian operational visibilityPythian operational visibility
Pythian operational visibility
 
GraphDay Paris - Intro & Découverte de l'outil de visualisation Neo4j Bloom
GraphDay Paris - Intro & Découverte de l'outil de visualisation Neo4j Bloom GraphDay Paris - Intro & Découverte de l'outil de visualisation Neo4j Bloom
GraphDay Paris - Intro & Découverte de l'outil de visualisation Neo4j Bloom
 
IBANK - Oracle developers-guide
IBANK - Oracle developers-guide IBANK - Oracle developers-guide
IBANK - Oracle developers-guide
 
GoldenGate and Oracle Data Integrator - A Perfect Match...
GoldenGate and Oracle Data Integrator - A Perfect Match...GoldenGate and Oracle Data Integrator - A Perfect Match...
GoldenGate and Oracle Data Integrator - A Perfect Match...
 
DataOps - Lean principles and lean practices
DataOps - Lean principles and lean practicesDataOps - Lean principles and lean practices
DataOps - Lean principles and lean practices
 
GraphConnect Europe 2016 - Governing Multichannel Services with Graphs - Albe...
GraphConnect Europe 2016 - Governing Multichannel Services with Graphs - Albe...GraphConnect Europe 2016 - Governing Multichannel Services with Graphs - Albe...
GraphConnect Europe 2016 - Governing Multichannel Services with Graphs - Albe...
 
Oracle ADWC
Oracle ADWCOracle ADWC
Oracle ADWC
 
Choosing the Right Open Source Database
Choosing the Right Open Source DatabaseChoosing the Right Open Source Database
Choosing the Right Open Source Database
 
Pldc2012 monitoring-and-trending-with-mysql
Pldc2012 monitoring-and-trending-with-mysqlPldc2012 monitoring-and-trending-with-mysql
Pldc2012 monitoring-and-trending-with-mysql
 
Oracle Autonomous Data Warehouse Cloud Webex - with Demo
Oracle Autonomous Data Warehouse Cloud Webex - with DemoOracle Autonomous Data Warehouse Cloud Webex - with Demo
Oracle Autonomous Data Warehouse Cloud Webex - with Demo
 
GraphTour - Closing Keynote
GraphTour - Closing KeynoteGraphTour - Closing Keynote
GraphTour - Closing Keynote
 
Data democratised
Data democratisedData democratised
Data democratised
 
Caveats of hosting MariaDB on Microsoft Azure Cloud
Caveats of hosting MariaDB on Microsoft Azure CloudCaveats of hosting MariaDB on Microsoft Azure Cloud
Caveats of hosting MariaDB on Microsoft Azure Cloud
 
M|18 How We Made the Move to MariaDB at FNI
M|18 How We Made the Move to MariaDB at FNIM|18 How We Made the Move to MariaDB at FNI
M|18 How We Made the Move to MariaDB at FNI
 

Viewers also liked

Survey on Frequent Pattern Mining on Graph Data - Slides
Survey on Frequent Pattern Mining on Graph Data - SlidesSurvey on Frequent Pattern Mining on Graph Data - Slides
Survey on Frequent Pattern Mining on Graph Data - SlidesKasun Gajasinghe
 
Mining Frequent Closed Graphs on Evolving Data Streams
Mining Frequent Closed Graphs on Evolving Data StreamsMining Frequent Closed Graphs on Evolving Data Streams
Mining Frequent Closed Graphs on Evolving Data StreamsAlbert Bifet
 
Frequent Itemset Mining(FIM) on BigData
Frequent Itemset Mining(FIM) on BigDataFrequent Itemset Mining(FIM) on BigData
Frequent Itemset Mining(FIM) on BigDataRaju Gupta
 
Data Mining: Concepts and Techniques chapter 07 : Advanced Frequent Pattern M...
Data Mining: Concepts and Techniques chapter 07 : Advanced Frequent Pattern M...Data Mining: Concepts and Techniques chapter 07 : Advanced Frequent Pattern M...
Data Mining: Concepts and Techniques chapter 07 : Advanced Frequent Pattern M...Salah Amean
 
Unlocking Success in the 3 Stages of Master Data Management
Unlocking Success in the 3 Stages of Master Data ManagementUnlocking Success in the 3 Stages of Master Data Management
Unlocking Success in the 3 Stages of Master Data ManagementPerficient, Inc.
 
9 Data Mining Challenges From Data Scientists Like You
9 Data Mining Challenges From Data Scientists Like You9 Data Mining Challenges From Data Scientists Like You
9 Data Mining Challenges From Data Scientists Like YouSalford Systems
 
Frequent itemset mining methods
Frequent itemset mining methodsFrequent itemset mining methods
Frequent itemset mining methodsProf.Nilesh Magar
 
10 Worst Practices in Master Data Management
10 Worst Practices in Master Data Management10 Worst Practices in Master Data Management
10 Worst Practices in Master Data Managementibi
 
Cyber security and attack analysis : how Cisco uses graph analytics
Cyber security and attack analysis : how Cisco uses graph analyticsCyber security and attack analysis : how Cisco uses graph analytics
Cyber security and attack analysis : how Cisco uses graph analyticsLinkurious
 
Digital Transformation in a Connected World
Digital Transformation in a Connected WorldDigital Transformation in a Connected World
Digital Transformation in a Connected WorldNeo4j
 
Neo4j graphs in the real world - graph days d.c. - april 14, 2015
Neo4j   graphs in the real world - graph days d.c. - april 14, 2015Neo4j   graphs in the real world - graph days d.c. - april 14, 2015
Neo4j graphs in the real world - graph days d.c. - april 14, 2015Neo4j
 
RDBMS to Graphs
RDBMS to GraphsRDBMS to Graphs
RDBMS to GraphsNeo4j
 
Graph Databases for Master Data Management
Graph Databases for Master Data ManagementGraph Databases for Master Data Management
Graph Databases for Master Data ManagementNeo4j
 
Using a Graph Database for Next-Gen MDM
Using a Graph Database for Next-Gen MDMUsing a Graph Database for Next-Gen MDM
Using a Graph Database for Next-Gen MDMNeo4j
 
IBM Graph – Graph Database-as-a-Service: Managing Data and Its Relationships ...
IBM Graph – Graph Database-as-a-Service: Managing Data and Its Relationships ...IBM Graph – Graph Database-as-a-Service: Managing Data and Its Relationships ...
IBM Graph – Graph Database-as-a-Service: Managing Data and Its Relationships ...Alexander Pozdneev
 
Create a 'Customer 360' with Master Data Management for Financial Services
Create a 'Customer 360' with Master Data Management for Financial ServicesCreate a 'Customer 360' with Master Data Management for Financial Services
Create a 'Customer 360' with Master Data Management for Financial ServicesPerficient, Inc.
 
Relational to Big Graph
Relational to Big GraphRelational to Big Graph
Relational to Big GraphNeo4j
 
360° View of Your Customers
360° View of Your Customers360° View of Your Customers
360° View of Your CustomersOSF Commerce
 
Interesting applications of graph theory
Interesting applications of graph theoryInteresting applications of graph theory
Interesting applications of graph theoryTech_MX
 

Viewers also liked (20)

Survey on Frequent Pattern Mining on Graph Data - Slides
Survey on Frequent Pattern Mining on Graph Data - SlidesSurvey on Frequent Pattern Mining on Graph Data - Slides
Survey on Frequent Pattern Mining on Graph Data - Slides
 
Mining Frequent Closed Graphs on Evolving Data Streams
Mining Frequent Closed Graphs on Evolving Data StreamsMining Frequent Closed Graphs on Evolving Data Streams
Mining Frequent Closed Graphs on Evolving Data Streams
 
Frequent Itemset Mining(FIM) on BigData
Frequent Itemset Mining(FIM) on BigDataFrequent Itemset Mining(FIM) on BigData
Frequent Itemset Mining(FIM) on BigData
 
Data Mining: Concepts and Techniques chapter 07 : Advanced Frequent Pattern M...
Data Mining: Concepts and Techniques chapter 07 : Advanced Frequent Pattern M...Data Mining: Concepts and Techniques chapter 07 : Advanced Frequent Pattern M...
Data Mining: Concepts and Techniques chapter 07 : Advanced Frequent Pattern M...
 
Unlocking Success in the 3 Stages of Master Data Management
Unlocking Success in the 3 Stages of Master Data ManagementUnlocking Success in the 3 Stages of Master Data Management
Unlocking Success in the 3 Stages of Master Data Management
 
9 Data Mining Challenges From Data Scientists Like You
9 Data Mining Challenges From Data Scientists Like You9 Data Mining Challenges From Data Scientists Like You
9 Data Mining Challenges From Data Scientists Like You
 
Frequent itemset mining methods
Frequent itemset mining methodsFrequent itemset mining methods
Frequent itemset mining methods
 
10 Worst Practices in Master Data Management
10 Worst Practices in Master Data Management10 Worst Practices in Master Data Management
10 Worst Practices in Master Data Management
 
5 Steps To Master Data Management
5 Steps To Master Data Management5 Steps To Master Data Management
5 Steps To Master Data Management
 
Cyber security and attack analysis : how Cisco uses graph analytics
Cyber security and attack analysis : how Cisco uses graph analyticsCyber security and attack analysis : how Cisco uses graph analytics
Cyber security and attack analysis : how Cisco uses graph analytics
 
Digital Transformation in a Connected World
Digital Transformation in a Connected WorldDigital Transformation in a Connected World
Digital Transformation in a Connected World
 
Neo4j graphs in the real world - graph days d.c. - april 14, 2015
Neo4j   graphs in the real world - graph days d.c. - april 14, 2015Neo4j   graphs in the real world - graph days d.c. - april 14, 2015
Neo4j graphs in the real world - graph days d.c. - april 14, 2015
 
RDBMS to Graphs
RDBMS to GraphsRDBMS to Graphs
RDBMS to Graphs
 
Graph Databases for Master Data Management
Graph Databases for Master Data ManagementGraph Databases for Master Data Management
Graph Databases for Master Data Management
 
Using a Graph Database for Next-Gen MDM
Using a Graph Database for Next-Gen MDMUsing a Graph Database for Next-Gen MDM
Using a Graph Database for Next-Gen MDM
 
IBM Graph – Graph Database-as-a-Service: Managing Data and Its Relationships ...
IBM Graph – Graph Database-as-a-Service: Managing Data and Its Relationships ...IBM Graph – Graph Database-as-a-Service: Managing Data and Its Relationships ...
IBM Graph – Graph Database-as-a-Service: Managing Data and Its Relationships ...
 
Create a 'Customer 360' with Master Data Management for Financial Services
Create a 'Customer 360' with Master Data Management for Financial ServicesCreate a 'Customer 360' with Master Data Management for Financial Services
Create a 'Customer 360' with Master Data Management for Financial Services
 
Relational to Big Graph
Relational to Big GraphRelational to Big Graph
Relational to Big Graph
 
360° View of Your Customers
360° View of Your Customers360° View of Your Customers
360° View of Your Customers
 
Interesting applications of graph theory
Interesting applications of graph theoryInteresting applications of graph theory
Interesting applications of graph theory
 

Similar to New opportunities for connected data : Neo4j the graph database

Big data hadoop-no sql and graph db-final
Big data hadoop-no sql and graph db-finalBig data hadoop-no sql and graph db-final
Big data hadoop-no sql and graph db-finalramazan fırın
 
GraphTalk Berlin - Einführung in Graphdatenbanken
GraphTalk Berlin - Einführung in GraphdatenbankenGraphTalk Berlin - Einführung in Graphdatenbanken
GraphTalk Berlin - Einführung in GraphdatenbankenNeo4j
 
Architecting Your Own DBaaS in a Private Cloud with EM12c
Architecting Your Own DBaaS in a Private Cloud with EM12cArchitecting Your Own DBaaS in a Private Cloud with EM12c
Architecting Your Own DBaaS in a Private Cloud with EM12cGustavo Rene Antunez
 
Neo4j PartnerDay Amsterdam 2017
Neo4j PartnerDay Amsterdam 2017Neo4j PartnerDay Amsterdam 2017
Neo4j PartnerDay Amsterdam 2017Neo4j
 
GraphTalk München - Einführung in Graphdatenbanken und Neo4j
GraphTalk München - Einführung in Graphdatenbanken und Neo4jGraphTalk München - Einführung in Graphdatenbanken und Neo4j
GraphTalk München - Einführung in Graphdatenbanken und Neo4jNeo4j
 
Oracle NoSQL Database -- Big Data Bellevue Meetup - 02-18-15
Oracle NoSQL Database -- Big Data Bellevue Meetup - 02-18-15Oracle NoSQL Database -- Big Data Bellevue Meetup - 02-18-15
Oracle NoSQL Database -- Big Data Bellevue Meetup - 02-18-15Dave Segleau
 
Neo4j GraphTalk Frankfurt - Identity und Access Management
Neo4j GraphTalk Frankfurt - Identity und Access ManagementNeo4j GraphTalk Frankfurt - Identity und Access Management
Neo4j GraphTalk Frankfurt - Identity und Access ManagementNeo4j
 
Difference between data warehouse and data mining
Difference between data warehouse and data miningDifference between data warehouse and data mining
Difference between data warehouse and data miningmaxonlinetr
 
Neo4j GraphDay Tel Aviv - Graphs in Action
Neo4j GraphDay Tel Aviv - Graphs in ActionNeo4j GraphDay Tel Aviv - Graphs in Action
Neo4j GraphDay Tel Aviv - Graphs in ActionNeo4j
 
Neo4j in Production: A look at Neo4j in the Real World
Neo4j in Production: A look at Neo4j in the Real WorldNeo4j in Production: A look at Neo4j in the Real World
Neo4j in Production: A look at Neo4j in the Real WorldNeo4j
 
Neo4j 4 Overview
Neo4j 4 OverviewNeo4j 4 Overview
Neo4j 4 OverviewNeo4j
 
Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 R...
Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 R...Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 R...
Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 R...Acunu
 
Introduction to the Typesafe Reactive Platform
Introduction to the Typesafe Reactive PlatformIntroduction to the Typesafe Reactive Platform
Introduction to the Typesafe Reactive PlatformBoldRadius Solutions
 
SQL, NoSQL, BigData in Data Architecture
SQL, NoSQL, BigData in Data ArchitectureSQL, NoSQL, BigData in Data Architecture
SQL, NoSQL, BigData in Data ArchitectureVenu Anuganti
 
Novo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4jNovo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4jNeo4j
 
Gluent Extending Enterprise Applications with Hadoop
Gluent Extending Enterprise Applications with HadoopGluent Extending Enterprise Applications with Hadoop
Gluent Extending Enterprise Applications with Hadoopgluent.
 
Neo4j GraphTalks - Einführung in Graphdatenbanken
Neo4j GraphTalks - Einführung in GraphdatenbankenNeo4j GraphTalks - Einführung in Graphdatenbanken
Neo4j GraphTalks - Einführung in GraphdatenbankenNeo4j
 
Notes from the Field - Cloud Solutions with VMware vCloud Director
Notes from the Field - Cloud Solutions with VMware vCloud DirectorNotes from the Field - Cloud Solutions with VMware vCloud Director
Notes from the Field - Cloud Solutions with VMware vCloud DirectorJames Charter
 

Similar to New opportunities for connected data : Neo4j the graph database (20)

Big data hadoop-no sql and graph db-final
Big data hadoop-no sql and graph db-finalBig data hadoop-no sql and graph db-final
Big data hadoop-no sql and graph db-final
 
GraphTalk Berlin - Einführung in Graphdatenbanken
GraphTalk Berlin - Einführung in GraphdatenbankenGraphTalk Berlin - Einführung in Graphdatenbanken
GraphTalk Berlin - Einführung in Graphdatenbanken
 
Architecting Your Own DBaaS in a Private Cloud with EM12c
Architecting Your Own DBaaS in a Private Cloud with EM12cArchitecting Your Own DBaaS in a Private Cloud with EM12c
Architecting Your Own DBaaS in a Private Cloud with EM12c
 
Neo4j PartnerDay Amsterdam 2017
Neo4j PartnerDay Amsterdam 2017Neo4j PartnerDay Amsterdam 2017
Neo4j PartnerDay Amsterdam 2017
 
GraphTalk München - Einführung in Graphdatenbanken und Neo4j
GraphTalk München - Einführung in Graphdatenbanken und Neo4jGraphTalk München - Einführung in Graphdatenbanken und Neo4j
GraphTalk München - Einführung in Graphdatenbanken und Neo4j
 
Telvent Big Data Approach and Case Studies
Telvent Big Data Approach and Case StudiesTelvent Big Data Approach and Case Studies
Telvent Big Data Approach and Case Studies
 
Oracle NoSQL Database -- Big Data Bellevue Meetup - 02-18-15
Oracle NoSQL Database -- Big Data Bellevue Meetup - 02-18-15Oracle NoSQL Database -- Big Data Bellevue Meetup - 02-18-15
Oracle NoSQL Database -- Big Data Bellevue Meetup - 02-18-15
 
Neo4j GraphTalk Frankfurt - Identity und Access Management
Neo4j GraphTalk Frankfurt - Identity und Access ManagementNeo4j GraphTalk Frankfurt - Identity und Access Management
Neo4j GraphTalk Frankfurt - Identity und Access Management
 
Difference between data warehouse and data mining
Difference between data warehouse and data miningDifference between data warehouse and data mining
Difference between data warehouse and data mining
 
Data Vault Introduction
Data Vault IntroductionData Vault Introduction
Data Vault Introduction
 
Neo4j GraphDay Tel Aviv - Graphs in Action
Neo4j GraphDay Tel Aviv - Graphs in ActionNeo4j GraphDay Tel Aviv - Graphs in Action
Neo4j GraphDay Tel Aviv - Graphs in Action
 
Neo4j in Production: A look at Neo4j in the Real World
Neo4j in Production: A look at Neo4j in the Real WorldNeo4j in Production: A look at Neo4j in the Real World
Neo4j in Production: A look at Neo4j in the Real World
 
Neo4j 4 Overview
Neo4j 4 OverviewNeo4j 4 Overview
Neo4j 4 Overview
 
Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 R...
Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 R...Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 R...
Cassandra EU 2012 - Overview of Case Studies and State of the Market by 451 R...
 
Introduction to the Typesafe Reactive Platform
Introduction to the Typesafe Reactive PlatformIntroduction to the Typesafe Reactive Platform
Introduction to the Typesafe Reactive Platform
 
SQL, NoSQL, BigData in Data Architecture
SQL, NoSQL, BigData in Data ArchitectureSQL, NoSQL, BigData in Data Architecture
SQL, NoSQL, BigData in Data Architecture
 
Novo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4jNovo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4j
 
Gluent Extending Enterprise Applications with Hadoop
Gluent Extending Enterprise Applications with HadoopGluent Extending Enterprise Applications with Hadoop
Gluent Extending Enterprise Applications with Hadoop
 
Neo4j GraphTalks - Einführung in Graphdatenbanken
Neo4j GraphTalks - Einführung in GraphdatenbankenNeo4j GraphTalks - Einführung in Graphdatenbanken
Neo4j GraphTalks - Einführung in Graphdatenbanken
 
Notes from the Field - Cloud Solutions with VMware vCloud Director
Notes from the Field - Cloud Solutions with VMware vCloud DirectorNotes from the Field - Cloud Solutions with VMware vCloud Director
Notes from the Field - Cloud Solutions with VMware vCloud Director
 

Recently uploaded

CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesSinan KOZAK
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
Next-generation AAM aircraft unveiled by Supernal, S-A2
Next-generation AAM aircraft unveiled by Supernal, S-A2Next-generation AAM aircraft unveiled by Supernal, S-A2
Next-generation AAM aircraft unveiled by Supernal, S-A2Hyundai Motor Group
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphNeo4j
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhisoniya singh
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024Scott Keck-Warren
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsHyundai Motor Group
 
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxnull - The Open Security Community
 

Recently uploaded (20)

CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
Next-generation AAM aircraft unveiled by Supernal, S-A2
Next-generation AAM aircraft unveiled by Supernal, S-A2Next-generation AAM aircraft unveiled by Supernal, S-A2
Next-generation AAM aircraft unveiled by Supernal, S-A2
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
The transition to renewables in India.pdf
The transition to renewables in India.pdfThe transition to renewables in India.pdf
The transition to renewables in India.pdf
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
 
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
 

New opportunities for connected data : Neo4j the graph database

  • 1. Cédric Fauvet Cedric.fauvet@neotechnology.com Twitter Francophone: @Neo4jFr 1 Confidential - Neo Technology, Inc. New opportunities for connected data : Neo4j the graph database
  • 2. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
  • 3. The graph theory An 840 : The horeseman’s problem The Arab mathematician and chess master al-Adli ar-Rumi solved the problem.
  • 4. The graph theory An 1735 The Königsberg’s 7 bridges problem How to pass through the bridges only once ? Leonhard Euler Swiss mathematician
  • 5. The graph theory 2013: Today’s questions • Collaboration • Configuration management • Geo mapping • Molecule’s Interaction (Biology) • Impact analysis • Master Data Management • Product management • Recommendation • Social
  • 6. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
  • 7. Neo Technology (Neo4j) Corporate Overview • Neo4j founded 2000 • Headquartered in Palo Alto, California • Engineering headquarter in Malmö, Sweden • Employees based in France, Germany, UK, Sweden, US, and Malaysia • 24/7 support on global basis • 100,000+ users • F500 customers such as Adobe, Cisco, Deutsche Telecom, Telenor, Deutsche Post, SFR, Lockheed Martin, and others • SI partners such as Accenture and dozens of local SI boutiques • Technology partners such as VMware, Informatica and Microsoft • Leader in the Graph Database arena • Mission: Help the world to make sense of data
  • 8. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
  • 9. Société - Worldwide company - 45 millions users, + 30 000 each days. - Owner of the social networks ApnaCircle (Inde) and Tianji (Chine) Problème Viadeo, integrated Neo4j as their backend database, to store all of their users and relationships. When their network expanded to a level that their traditional MySQL database couldn’t handle, Viadeo experienced performance and storage issues that would not perform at the rate the company was growing. Etude de cas: Réseau social Bénéfices & time frame - Real time recomendation with Neo4j. - Project timeframe = 8 weeks Solution Integrating Neo4j, Viadeo has highly accelerated their system in two ways. Neo4j increased Viadeo’s performance by requiring less storage space andless time to restructure the graph.
  • 10. 10 Company - Worldwide leader in networking for the Internet Solution - Clustered Neo4j Enterprise architecture - Part of a larger infrastructure solution - Multi-region AWS deployment - Neo4j selected in competition with custom solution and Oracle Benefits & time frame - Highly flexible data analysis - Sub-second results for large, densely-connected datasets - User experience - competitive advantage - 12 month project Problem definition - Massive amounts of data tied to members, user groups, member content, etc. all interconnected - Need to infer collaborative relationships based on user-generated content Case study: Web/ISV - social collaboration Adobe
  • 11. 11 Company - Leading telco provider in the Nordics Solution - Neo4j Enterprise solution - Embedded + HA - Replacing 10 yr-old Oracle, Berkeley DB and a mainframe environment Problem definition - Need: Reliable access control administration system for 5mio customers, subscriptions and agreements - Complex dependencies between groups, companies, individuals, accounts, products, s ubscriptions, services and agreements - Broad and deep graphs (master customers with 1000s of customers, subscriptions & agreements) Case study: Telco Telenor Benefits & time frame - Flexible and dynamic architecture - Exceptional performance -Low cost compared to alternatives -Extensible data model supports new applications and features
  • 12. 12 Company -World wide leader in network infrastructure -Large sales organization Solution -2x Highly Available Neo4j clusters -One live cluster and one backup / hot spare cluster at a different datacenter -Total: 6 Embedded Enterprise Neo4j DBs Benefits & timeframe -Real time overview of sales accounts and owners -The ability to model complex rules for account ownership -Direct commissioning computation through the entire sales organization ->12 month development and rollout Problem definition -Intricate rules governing ownership of sales accounts -Complex rules for sales commissions -Queries complicated to structure with RDBMS -Oracle performance not good enough for online account management Case study: Sales account management Cisco
  • 13. Use case – What’s in common ? Alice ACME ACME EMEA Bob Retail Co. FooBar Inc. Sales Rep Sales Rep Worked For Worked For Sold To
  • 14. Use case – What’s a best path ? Retail Co. Bob ACME Steve Jane Liza Pauline William Sales Rep VP CMO Sales Rep VP
  • 15. Use case : Pattern matching Fraud detection
  • 22. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
  • 23. Trend 1:Exponential growth of data 0 250 500 750 1000 2007 2008 2009 2010 Exabytes of new unique digital information
  • 24.
  • 25. size * connectivity = complexity
  • 26.
  • 27. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
  • 28. Neo4j Tackles complex data: – Large – Densely-connected – Semi-structured
  • 29. Neo4j characteristics • Fully ACID – Including XA-compliant distributed two-phase commits • High Availability / Read Scaling through master-slave replication with master failover • In-memory speeds with warm caches while maintaining full ACID • Cypher query language and Java APIs
  • 30. Caractéristiques de Neo4j • Transactions Full ACID – XA-compliant distributed two-phase commits • Haute disponibilité / Scalabilité* – master-slave réplication avec master Fail-over – * Lecture • Hautes performance en mémoire – Caches évolués full ACID • Langage des requêtes – Cypher – Java APIs – JDBC – Rest API – Ruby
  • 31. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
  • 32. () --> () Cypher the Neo4j’s « SQL » Based on ACSII-Art
  • 33. (A) --> (B) A B Cypher the Neo4j’s « SQL » Each node have a identifier
  • 34. A -[:LOVES]-> B LOVES A B Cypher the Neo4j’s « SQL » Relationship
  • 35. A --> B --> C A B C Cypher the Neo4j’s « SQL » You can traverse the graph
  • 36. A -[*]-> B A B A B A B Cypher the Neo4j’s « SQL » You can dynamically traverse the graph
  • 37. Cypher the Neo4j’s « SQL » The friend of friend query START john=node:node_auto_index(name = 'John') MATCH john-[:friend]->()-[:friend]->fof RETURN john, fof
  • 38. Thank you Let’s move forward together ! Cédric Fauvet Your contact in France and switzerland E-mail : Cedric.fauvet@neotechnology.com French speaking Twitter : @Neo4jFr French speaking community : meetup.com/graphdb-france

Editor's Notes

  1. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  2. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  3. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  4. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  5. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  6. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  7. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  8. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  9. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  10. Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS