In this, we discuss about following Points:
1. What is Big -Data
2. Why Graph Database involved?
3. What is Neo4J?
4. Neo4J Cypher Query Language.
5. Spring-Data-Neo4j Sample Application.
This presentation covers several aspects of modeling data and domains with a graph database like Neo4j. The graph data model allows high fidelity modeling. Using the first class relationships of the graph model allow to use much higher forms of normalization than you would use in a relational database.
Video here: https://vimeo.com/67371996
Graph Database Defined. A graph database is defined as a specialized, single-purpose platform for creating and manipulating graphs. Graphs contain nodes, edges, and properties, all of which are used to represent and store data in a way that relational databases are not equipped to do.
3rd Athens Big Data Meetup - 2nd Talk - Neo4j: The World's Leading Graph DBAthens Big Data
Title: Neo4j: The World's Leading Graph DB
Speaker: George Eleftheriadis (https://gr.linkedin.com/in/george-eleftheriadis-4526ba51/)
Date: Monday, April 18, 2016
Event: https://meetup.com/Athens-Big-Data/events/229812890/
Have you heard about all the hot new features in SQL Server 2017? One of the game-changing features is Graph DB. Learn what it is, how you can use it, and what scenarios it excels in - specifically where data has strongly defined relationships and is more interconnected.
Asynchronous single page applications without a line of HTML or Javascript, o...Robert Schadek
AngularJS, together with Node.js, is an extremely powerful combination for building single page applications. Unfortunately, its development requires writing HTML and Javascript, which is tedious and error prone. By using vibe.d, HTML is no longer necessary, and the developers can use the full power of a static-typed language for the development of the backend. Substituting Javascript with Typescript in addition to a little bit of CTFE D magic then removes the need for redundant data type declarations, and makes everything statically typed. At the end of the talk, the attendee will have witnessed the creation of a statically typed, asynchronous single page application that required little extra typing than its dynamically typed equivalent. Additionally, the attendees will be motivated to explore the presented combination of frameworks as a viable desktop application UI framework.
Neo4j Morpheus: Interweaving Table and Graph Data with SQL and Cypher in Apac...Databricks
Graph data and graph analytics are increasingly important in data science and engineering. Cypher is an open language used for querying and updating graph databases and analytics platforms, which is now available in the Apache Spark environment. Neo4j Morpheus leverages the open source graph language project to integrate data from Neo4j operational graph databases with Hive and JDBC SQL data sources, using new Cypher features like the Property Graph Catalog, named graphs, graph projection, parameterized graph view functions, and graph/table views. Input and output graphs can be loaded and stored as structured collections of DataFrames with strong graph schemas to ensure data consistency and graph query optimization. Property graphs can also be analyzed and transformed using graph algorithms such as those in the GraphFrames project. Besides describing and demonstrating these capabilities, this talk also discusses the Spark Project Improvement Proposal to bring Cypher into Spark 3.0, and outlines current work to unify Cypher with other graph query languages to form a new ISO standard Graph Query Language.
Speakers: Alastair Green, Martin Junghanns
In this webinar Thomas Cook, Sales Director, AnzoGraph DB, provides a history lesson on the origins of SPARQL, including its roots in the Semantic Web, and how linked open data is used to create Knowledge Graphs. Then, he dives into "What is RDF?", "What is a URI?" and "What is SPARQL?", wrapping up with a real-world demonstration via a Zeppelin notebook.
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
The openCypher Project - An Open Graph Query LanguageNeo4j
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
This presentation covers several aspects of modeling data and domains with a graph database like Neo4j. The graph data model allows high fidelity modeling. Using the first class relationships of the graph model allow to use much higher forms of normalization than you would use in a relational database.
Video here: https://vimeo.com/67371996
Graph Database Defined. A graph database is defined as a specialized, single-purpose platform for creating and manipulating graphs. Graphs contain nodes, edges, and properties, all of which are used to represent and store data in a way that relational databases are not equipped to do.
3rd Athens Big Data Meetup - 2nd Talk - Neo4j: The World's Leading Graph DBAthens Big Data
Title: Neo4j: The World's Leading Graph DB
Speaker: George Eleftheriadis (https://gr.linkedin.com/in/george-eleftheriadis-4526ba51/)
Date: Monday, April 18, 2016
Event: https://meetup.com/Athens-Big-Data/events/229812890/
Have you heard about all the hot new features in SQL Server 2017? One of the game-changing features is Graph DB. Learn what it is, how you can use it, and what scenarios it excels in - specifically where data has strongly defined relationships and is more interconnected.
Asynchronous single page applications without a line of HTML or Javascript, o...Robert Schadek
AngularJS, together with Node.js, is an extremely powerful combination for building single page applications. Unfortunately, its development requires writing HTML and Javascript, which is tedious and error prone. By using vibe.d, HTML is no longer necessary, and the developers can use the full power of a static-typed language for the development of the backend. Substituting Javascript with Typescript in addition to a little bit of CTFE D magic then removes the need for redundant data type declarations, and makes everything statically typed. At the end of the talk, the attendee will have witnessed the creation of a statically typed, asynchronous single page application that required little extra typing than its dynamically typed equivalent. Additionally, the attendees will be motivated to explore the presented combination of frameworks as a viable desktop application UI framework.
Neo4j Morpheus: Interweaving Table and Graph Data with SQL and Cypher in Apac...Databricks
Graph data and graph analytics are increasingly important in data science and engineering. Cypher is an open language used for querying and updating graph databases and analytics platforms, which is now available in the Apache Spark environment. Neo4j Morpheus leverages the open source graph language project to integrate data from Neo4j operational graph databases with Hive and JDBC SQL data sources, using new Cypher features like the Property Graph Catalog, named graphs, graph projection, parameterized graph view functions, and graph/table views. Input and output graphs can be loaded and stored as structured collections of DataFrames with strong graph schemas to ensure data consistency and graph query optimization. Property graphs can also be analyzed and transformed using graph algorithms such as those in the GraphFrames project. Besides describing and demonstrating these capabilities, this talk also discusses the Spark Project Improvement Proposal to bring Cypher into Spark 3.0, and outlines current work to unify Cypher with other graph query languages to form a new ISO standard Graph Query Language.
Speakers: Alastair Green, Martin Junghanns
In this webinar Thomas Cook, Sales Director, AnzoGraph DB, provides a history lesson on the origins of SPARQL, including its roots in the Semantic Web, and how linked open data is used to create Knowledge Graphs. Then, he dives into "What is RDF?", "What is a URI?" and "What is SPARQL?", wrapping up with a real-world demonstration via a Zeppelin notebook.
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
The openCypher Project - An Open Graph Query LanguageNeo4j
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Graph Database Using Neo4J
1. Graph Database
Using
Neo4J
By Harmeet Singh(Taara)
(Java EE Developer)
Email: harmeetsingh.0013@gmail.com
Website: http://programmers-nest.com
Blog: http://harmeetsingh13.blogspot.com
Skype: harmeetsingh0013
2. Contents
➔ Introduction
➔ Big Data
➔ Graph Databases
➔ Graph DB Vs RDBMS
➔ Journey: RDBMS To Graph DB Modeling
➔ Neo4J
➔ Cypher Query
◆ CREATE, MATCH, WHERE, SET, DELETE, RETURN, REMOVE
◆ Relationship
◆ ORDER BY, SKIP, LIMIT, DISTINCT
◆ Aggregation
➔ Spring-Data-Neo4J Sample
➔ Leftover: The things we didn't cover
3. Acknowledgement
➔ Thanks To My Parents.
➔ Thanks To All Who Support Me or Not.
➔ Dedicated To My Teacher “Mr. Kapil Sakhuja”.
4. Introduction
➔ Today, we discuss about Graph Database and Why
Graph Databases involved.
➔ How we use Graph Database using Neo4J.
➔ Cypher Query Language for Neo4J.
➔ Spring-Data-Neo4J Sample Application.
6. Graph Database
➔ A Graph Database is a set of vertices and
edges.
➔ Graph Databases is to view the data as an
arbitrary set of objects connected by one or
more kinds of relationships.
7. Graph DB Vs RDBMS
➔ RDBMS limitation on How a relationship is defined
within a relational database?
➔ In RDBMS creating a join table that brings together
two disparate tables is a common practice, doing so
adds a layer of complexity.
8. Graph DB Vs RDBMS
➔ A join table is created in order to have metadata that
provides properties about relationships between two
tables. When a similar relationship needs to be created
among other tables, yet another join table must be
created.
➔ Graph databases over relational database is to avoid
what might be referred to as “join hell”
9. Journey: RDBMS to Graph DB Modeling
➔ In RDBMS, the data is collected in form of Tables, and
the Tables are define with Rows And Columns.
➔ The single table contains Multiple Records and these
records are represent to real world Entity.
10. Journey: RDBMS to Graph DB Modeling
➔ Now, in Graph Database the data represent in the form
of Nodes and One node is compared to one record in
table.
➔ The Node type is compared to Entity.
➔ In Graph DB, we can create easy relationships with
nodes.
11. Neo4J
Necessity Is The Mother Of Invention
➔ Neo4j began its life in 2000, when Emil Eifrem, Johan
Svensson, and Peter Naubauer.
➔ World’s Best And First Graph Database.
12. Neo4J
➔ The “j” in Neo4j stands for Java, and the Java
Development Kit (JDK) is required to run it.
➔ Neo aimed to introduce a database that offered a
better way to model, store, and retrieve data while
keeping all of the core concepts—such as ACIDity,
transactions, and so forth—that made relational
databases into a proven commodity.
13. Cypher Query Language
➔ Cypher is the Declarative Query Language used for
data manipulation in Neo4j.
➔ A Declarative Language is a high-level type of
language in which the purpose is to instruct the
application on what needs to be done or what you
want from the application, as opposed to how to do it.
➔ Cypher is a Case Sensitive Language.
14. Cypher Query Language
➔ Cypher is a declarative, SQL-inspired language for
describing patterns in graphs. It allows us to describe
what we want to select, insert, update or delete from a
Graph Database without requiring us to describe
exactly how to do it.
➔ Cypher is not yet a standard graph database language
that can interact with other graph database platforms.
15. CREATE
➔ SQL
◆ INSERT INTO User (name, age) values
(“James”, 26)
➔ Cypher
◆ CREATE (u:User {name:"James",age:"26"})
RETURN u
16. MATCH
➔ SQL
◆ SELECT * FROM User
◆ SELECT u.name FROM USER u
➔ Cypher
◆ MATCH (u:User) RETURN u
◆ MATCH (u:User) RETURN u.name
17. WHERE
➔ SQL
◆ SELECT * FROM User u WHERE u.age = 26
➔ Cypher
◆ MATCH (u:User {age:26}) RETURN u
◆ MATCH (u:User) WHERE u.age = 26 RETURN u
18. SET
➔ SQL
◆ UPDATE User u SET u.age = 26 WHERE u.name
= “James”
◆ ALTER TABLE User ADD address varchar(45)
➔ Cypher
◆ MATCH (u:User {name:"James"}) SET u.age =
26 RETURN u
◆ MATCH (u:User {name:"James"}) SET u.
address = "Moga" RETURN u
19. DELETE
➔ SQL
◆ DELETE FROM User u WHERE u.name IS NULL
➔ Cypher
◆ MATCH(u:User) WHERE u.name IS NULL DELETE
u
➔ NOTE: If you delete a node that has relationships, you need
to be sure to remove the relationships as well
20. RETURN
➔ The RETURN is similar to the SELECT statement found
in SQL
➔ SQL
◆ SELECT u.name AS UserName, u.age AS Age
FROM User u
➔ Cypher
◆ MATCH(u:User) RETURN u.name AS UserName,
u.age AS Age
21. REMOVE
➔ SQL
◆ ALTER TABLE User u DROP COLUMN u.address
WHERE u.name = “James”
➔ Cypher
◆ MATCH(u:User {name:"James"}) REMOVE u.
address RETURN u
22. Relationships
➔ CREATE Relation
◆ Match (u:User {name:"James"}), (c:Company
{name:"Netsol"}) create (u)-[:EMP]-> (c)
◆ Match (u:User {id:1}), (u1:User {id:2})
create (u)-[:FRIEND {type:"Brothers"}]->
(u1) RETURN u, u1
➔ NOTE: By convention those relationship-types are written
all upper case using underscores between words.
24. Relationships
➔ MATCH
◆ MATCH(u:User) -[*]-> (u1:User) RETURN u,
u1
◆ MATCH(u:User) -[*1..5]-> (u1:User) RETURN
u, u1
◆ MATCH(u:User) -[*2]-> (u1:User) RETURN u,
u1
◆ MATCH(u:User) -[:FRIEND*2]-> (u1:User)
RETURN u, u1
25. DISTINCT, ORDER BY, SKIP, LIMIT
➔ SQL
◆ SELECT DISTINCT u.name FROM User u WHERE
u.age = 25 ORDER BY u.name DESC
OFFSET 0 LIMIT 5
➔ Cypher
◆ MATCH(u:User {age:25}) RETURN
DISTINCT u.name ORDER BY u.name DESC
SKIP 0 LIMIT 5
26. Aggregation
➔ COUNT
◆ MATCH(u:User {age:25}) RETURN COUNT(u.
name)
➔ COLLECT
◆ MATCH(u:User {age:25}) RETURN COLLECT(u.
name)
➔ NOTE: There are more aggregation functions like
min(), max(), avg() etc.
27. Spring-Data-Neo4j Sample
➔ Please access below link for Spring-Data-Neo4j Sample
Application.
◆ https://github.com/harmeetsingh0013/Spring-Data-
Neo4j-Example
28. Leftover: The things we didn’t cover
➔ Graph Theory
➔ Database ACID Operations
➔ Graph DB Modeling
➔ Advance Cypher Query Language
➔ Neo4j Aggregation Functions
➔ Neo4J Native Libraries With Java
➔ Neo4J Rest API
➔ Indexing
29. References
➔ Practical Neo4J By Gregory Jordan
Foreword By Jim Webber
➔ http://neo4j.com/top-ten-reasons/
➔ http://neo4j.com/developer/get-started/