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Neo4j Graph Database
Neo4j is a graph database management system developed by Neo Technology, Inc. Described as an ACID-compliant transactional database with native graph storage and processing, Neo4j is the most popular graph database according to DB-Engines ranking.As...
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Frequently asked questions

A graph database is a type of NoSQL database that stores data in the form of nodes and edges. Nodes represent entities, such as people or organizations, while edges are relationships between those entities. For example, an edge could be used to connect two nodes representing users who have “friended” each other on Facebook. The relationship itself can also contain additional information about how strong the connection is (e.g., whether it was initiated by one user or both). This structure allows for more complex queries than traditional relational databases allow because they don’t require you to join multiple tables together before performing your query; instead all related data can be queried at once using simple traversal algorithms like depth-first search and breadth-first search.

There are many types of Graph Databases. The most common ones include Property Graph, RDF and Triplestore. Each type has its own strengths and weaknesses depending on the use case you have in mind for your application or business process. For example, if you want to store data about people (e.g., their name, age) then a property graph is probably what you need; however if instead you’re interested in storing information about products (e.g., price), then an RDF database might be more suitable as it allows relationships between objects to be expressed using URIs rather than just simple properties like those found within a property graph model – this makes querying much easier when dealing with complex queries that involve multiple entities from different domains such as “find all books written by authors who live in London”.

Graph Databases are a new breed of database that have been gaining popularity in recent years. They provide the ability to model and query relationships between entities, which is something relational databases do not support out-of-the box. This makes them ideal for modeling complex data structures such as social networks or product catalogs where there are many different types of connections between objects (e.g., friends with other friends). In addition, they can be used to store unstructured data like documents and images because their flexible schema allows you to add attributes without having to define all your fields up front. Finally, graph databases allow users to traverse through connected nodes using paths instead of following predefined joins from one table/entity type into another; this enables more natural ways of querying large datasets than what’s possible with traditional SQL queries on relational tables alone.

The main disadvantage of a graph database is that it’s not as widely used or understood. This means there are fewer people who can help you with your problems, and less documentation available to guide you through the process. It also means that if something goes wrong, it may be harder for someone else to fix (or even understand) what went wrong in the first place.

Any company that needs to store and query relationships between entities. This includes companies in the financial, healthcare, retail or any other industry where data is stored as a graph of connected nodes. Graph databases are particularly useful for analyzing social networks (Facebook), tracking product sales over time (Amazon) or understanding how people interact with each other online (LinkedIn).

The first thing to consider is the problem you are trying to solve. If your data model has a lot of relationships, then it’s likely that graph databases will be useful for solving your problems. For example, if you have an e-commerce site and want to recommend products based on what customers bought in the past or where they live (geo-location), then a graph database would be appropriate because these types of queries require traversing through many different nodes and edges within your dataset. Another good use case for Graph Databases is when there are multiple entities with complex interrelationships between them – such as social networks like Facebook or Twitter which contain users who can follow each other; companies that can buy from one another; etc… In this scenario, using traditional relational databases may not provide enough flexibility since their schema design doesn’t allow us to easily add new attributes without having major impacts on existing applications/queries against our database(s). Finally, some people choose Graph Databases over RDBMSes simply because they don’t need ACID compliance guarantees nor do they care about transactional consistency across all writes made by concurrent clients at any given time (e.g., Google BigTable)

A graph database is implemented as a collection of nodes and edges. The nodes are the entities in your data, such as customers or products. Edges represent relationships between those entities, for example that one customer has placed an order on another product. In Neo4j these two types of objects are called “nodes” and “relationships” respectively (although they can be referred to by other names).

When you have a lot of data that is connected in some way. If your data has relationships, then it’s probably worth considering using a graph database.

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