Introducing Neo4j graph database
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The document provides an overview of Neo4j, a prominent graph database modeled to support connected data, released by Neo Technology in 2007. It highlights its features, such as ACID compliance, efficient data modeling using Cypher Query Language (CQL), and various use cases including fraud detection and real-time recommendations. Additionally, it compares graph databases with relational databases and outlines basic graph data modeling components.
![CQL
CREATE (:Person {name:”Pedram”, number:”095312345678”})
CREATE (:Person {name:”Sadra”, number:”0922123355”})
MATCH (p1:Person) WHERE p1.name = “Pedram”
MATCH (p2:Person) where p2.name = “Sadra”
CREATE (p2)-[:IN_REL]->(p1);
MATCH n RETURN n LIMIT 25
MATCH (p1:Person)-[r:IN_REL]-(p2:Person) return r
MATCH (p1:Person) DELETE p1
MATCH (p1:Person) where p1.name = “Pedram”
SET p1.number = “09133333333”
MATCH (p1:Person) where p1.name = “Pedram”
REMOVE p1.number](https://image.slidesharecdn.com/neo4j-170611093825/85/Introducing-Neo4j-graph-database-24-320.jpg)
Introducing Neo4j graph database
- 2. 1st Rank in Graph Database model Amirhossein Saberi - 2016
- 4. A Graph is Connected Data
- 5. A Graph is Connected Data
- 6. Use of Graphs has created some of the most successful companies in the world
- 7. Today we see graph-projects in virtually every industry
- 8. Introduction Developer: Neo Technology Initial release: 2007; 9 years ago Stable release: 3.0.0 / 19 days ago Written In: Java Type: Graph Database License: Dual licensed: GPLv3 and AGPLv3 / commercial Website: neo4j.com
- 9. Features Fully support ACID Using CQL (Cypher Query Language): 10x to 100x less code than SQL & Fast Elastic scaling Hot backup Cluster support In-memory cache Linux & Windows “Whiteboard Friendly” Data Modeling Native Graph Processing: Millions of hops per second, in real time Native Graph Storage: Ensures data consistency and performance
- 10. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Graph Based Search Network & IT-Operations Identity & Access Management
- 12. Fraud Detection
- 13. Relational DB vs Graph Darabase
- 14. Alice Loves Bob
- 15. Alice Loves Bob
- 19. Property Graph Model Components Nodes The objects in the graph Can have name-value properties Can be labeled Relationships Relate nodes by type and direction Can have name-value properties
- 20. Graph Data Model
- 23. Cypher
- 24. CQL CREATE (:Person {name:”Pedram”, number:”095312345678”}) CREATE (:Person {name:”Sadra”, number:”0922123355”}) MATCH (p1:Person) WHERE p1.name = “Pedram” MATCH (p2:Person) where p2.name = “Sadra” CREATE (p2)-[:IN_REL]->(p1); MATCH n RETURN n LIMIT 25 MATCH (p1:Person)-[r:IN_REL]-(p2:Person) return r MATCH (p1:Person) DELETE p1 MATCH (p1:Person) where p1.name = “Pedram” SET p1.number = “09133333333” MATCH (p1:Person) where p1.name = “Pedram” REMOVE p1.number
- 25. References http://db-engines.com/en/ranking/graph+dbms https://en.wikipedia.org/wiki/Neo4j http://www.tutorialspoint.com/neo4j/neo4j_features_advantages.htm http://neo4j.com/developer/graph-database/ http://www.slideshare.net/neo4j/intro-to-neo4j-and-graph-databases