What is Neo4j?

Neo4j is a native graph-based database language due to its native storage data structure which is a graph. In it, data elements are stored as nodes, and edges between nodes depict relationships between data elements. It uses the Cypher query language, designed explicitly for graph-based databases. Cypher cuts down the code complexity and length by removing join operations. Due to its structure, it performs well in handling graphical data by optimally traversing data through graph traversal algorithms. This is especially handy for complex queries requiring multiple traversals and access to different queries.

Moreover, Neo4j is also ACIDAtomicity, Consistency, Isolation, Durability-supportive, which ensures data integrity and consistency in its transactions. Additionally, it has been designed in a manner that it can scale horizontally, which enables it to handle large datasets and more significant queries concurrently. Due to these reasons, Neo4j is primarily used in social media applications and recommendation engines. Here, the speed and connection properties allow for a smoother link between the query engine and the requests.

Neo4j offers three main products relating to the graph-based database field.

1. Neo4j graph database: This self-managed database can be deployed anywhere. It allows for full administrative authority to the user as the responsibility for managing the database lies on them. Moreover, the user is also responsible for backups and updates. It is also the most used service as it offers users fast queries, easy scaling, and security.

2. Neo4j Aura DB: Unlike the self-managed option, Neo4j Aura DB is a fully managed database service that provides an easier and less stressful experience. It allows its users to focus solely on their applications and data while the managerial work is handled behind the scenes. This managed service offers the same features as the self-managed Neo4j Graph Database, such as fast queries, easy scaling, and robust security, but with the added advantage of automated maintenance and monitoring. This is the desired option for those who require a graph-based database but cannot manage or do not want to manage their database.

3. Neo4j graph data science: This is a machine learning tool that can use the Neo4j database ecosystem to analyze relationships and, in turn, improve predictions. Moreover, its visualization capabilities make it an excellent tool for understanding and predicting results through visuals. It can also export its models into external pipelines, greatly simplifying the process.

Neo4j's graphical base makes it a fast database model, creating a high demand in high-traffic use cases. We will discuss some of the below.

1. Social networking and recommendation: Primarily, Neo4j is used for social media and recommendation applications. This is due to the fast-paced query system, which does not need an index tree to find different relationships but can use its native graph data structure to find relationships between multiple data elements quickly.

2. Fraud detection: The graphical structure of Neo4j allows us to detect patterns and connections among clients, transactions, banks, and much more which in turn allows us to identify malicious activities.

3. Medicine: Another platform where patterns can be helpful is in medicine and healthcare. Finding patterns and dependencies between medical conditions, medicine, symptoms, and other indicators can help doctors understand the issues and lead to a better diagnosis with more information.

4. Network infrastructure: Using graphical visualizations, we can represent network devices like routers and servers as nodes. This allows us to visualize dependencies while also looking at their data elements. This can help us optimize our networks and make decisions based on visual and calculated values.

Let us review a database and query example to learn how everything works.

First, we start by creating nodes in our database. The syntax follows the pattern where the CREATE keyword creates a label User with its respective name. It also maps the property set for the node, i.e., it maps Alice to the name property. A visual representation is given below.

Second, we create links or edges or relationships between our nodes. This establishes how nodes or data elements are linked together. In this example, we CREATE an edge between our first user, alice, with another user, i.e., bob. It also sets the value for the relationship type between the two as FRIENDS_WITH.

Our example database and its relationships are now complete. To test it out, let us run a search query on it to see its results. In our example below, we are using a MATCH query to return all the friends or FRIENDS_WITH of alice. Moreover, we return them in a cleaner format by using the friend.name format.

Note: To run these commands on our own, visit the Neo4j console page and start now.

Neo4j is a powerful graphical database that allows efficient and quick queries over large amounts of data. Moreover, its adherence to ACID protocols and its ability to scale puts it in a favorable position. Finally, its ability to provide real-time analytics can be a deal-maker for those needing a fast system. Overall, Neo4j provides an excellent choice for those looking to create a fast, load-bearing, and complex model to add to their database.