Graph Databases and Neo4j


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Graph Databases and Neo4j
submitted by: lior

Graph Database and Neo4j

In the smaller village that called world, networking, relationships and connectivity become a must.

Relational databases that were built to codify forms and tabular data - lack the ability to represent relationships.
When coming to represent the quality, direction or weight/strength of the relations they perform poorly.

Data itself become much more complex and can hardly be put into table like databases.
The social media era with apps like Facebook and Whatsapp brought us a network of information that a relational database is struggling very hard to represent .
We work against the the real world when we try to model connectivity in a relational database.

Even a simple question like "Who is a friend of Bob’s friend?" will end up in double join query. Let alone when we want to go deeper to the relations of Bob. NoSQL also handles relationships poorly and as so lacks the flexibility of handling networking, tree type data or linked-data


Graph Database

Graph Database puts emphasis on relationships and connectivity. Projects that want to use social data, networking, decision trees, customer/supplier CRM or development management line etc., can earn much by using this kind of DB.

Another important benefit is the natural language being used (Cypher). If I wanted to save in the DB that “Inbal (person) works In Linnovate (company)” I will use:

Create (inbal:Person {name:’Inbal’})-[:WorksIn]-> (linnovate:Company {name:’Linnovate’})

The graph would look like this:



And if Tom also works for Linnovate. The graph will show:

That bring us to the third advantage which is the visualization Of the data.


Some Uses

Master Data Management

Organizations and product lines are inherently shaped like graphs: deep hierarchies with top-down, lateral, and diagonal connections:


Network and IT Operations

The interconnected physical, virtual, and application layers of a network are perfectly modeled in a comprehensive Neo4j graph:



Real-Time Recommendations

Connect the dots of seemingly unrelated interests and relationships to make recommendations that balance fresh with familiar:

Social Network

Family, friends and followers extend into a social graph which reveals patterns of similar behavior, influence, and implicit groups. Graph-based-search, managing your organization’s growing library of digital assets requires a more robust search solution. With Neo4j graph-based search tools your queries return more accurate and relevant real-time results.

You can see some top use cases Here



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