There are many different types of databases today. There could be a relational database, document DB, object-oriented databases, and many others.

When you look at the popular database that we most commonly use, the relational database, is a way of representing the data in tables. In a relational database, each row in the table is a record with a unique ID called the key. The columns of the table hold attributes of the data, and each record usually has a value for each attribute, making it easy to establish relationships among data points.

That’s great to be used when the relations are held for a single level. For example, to retrieve the details of a particular student given the unique id of the student. So, it can be said that the other details of the student are functionally dependent on the attribute Student ID.

These might all be popular, but they have their own strengths. But one place that they fall in short is when handling complex connections between the data. Of course, we can have functional dependencies and can extract the data through queries. But what if we have large connections? Would our query processing be able to work? Even if it works, would we be able to comprehend the heavy relationship? With all this, the process becomes obviously slow. This is where the aspect of a Graph Database comes into play. Unlike the other forms of databases, a graph database doesn’t need to compute the relationship between the data in the query time. The connections are already stored right in the database.

Through a specialized design for itself, which we are going to study in a while, it has a well-established structure depicting the nodes, edges, labels, and the relationship between all these properties. It is because of this that one can also do information retrieval and query processing very efficiently.

A graph database is a form of NoSQL database, as the name suggests, it (Not SQL or Not Only SQL) is a generic term used for databases that do not depend on a relational model. The data does not need a strict schema or the usual SQL table structure. Most commonly, the data is aggregated as key-value pairs, JSON documents, graphs, or wide-column tables. By using NoSQL databases, you can store immense volumes of unstructured data as it comes in and structures it at a later point. As expected, this leads to much better throughput, and read/write speeds, and allows you to scale out servers horizontally. Non-relational databases, when applied in the right use-case environment, bring significant benefits in terms of performance and flexibility. However, not applying a schema at the data entry point also means it is more difficult to query NoSQL databases, maintain data consistency, and establish relationships between data sets.

A graph database approach allows a deeper interconnection exploration of data and by providing more detailed answers to some of the complex questions about how data points relate to each other.

Graph databases are not meant to replace relational databases. As of now, relational databases are the industry standard. The most important aspect is to know what each database type has to offer. Relational databases provide a structured approach to data, whereas graph databases are agile and focus on quick data relationship insight.

For those types of data that have a process-oriented approach and store the data in a structured format, relational databases work the best. When you have relationships connected between the attributes, there are a lot of connections established between them, then Graph databases have greater use. For a practical implementation of Graph Databases, stay tuned for the upcoming blogs.

What is Neo4j?

Neo4j Graph Database The most trusted, secure, and globally deployed graph database. At a real-world scale, you get no-compromise performance, reliability, and data integrity. Battle-tested for performance, Neo4j is the only enterprise-strength graph database that combines native graph storage, scalable speed-optimized architecture, and ACID compliance. The high-performance distributed cluster architecture of Neo4j enables the most challenging OLTP and data science workloads. With Neo4j, you can choose from multiple cloud options – self-hosted, hybrid, multi-cloud, or our fully managed cloud service, Neo4j AuraDB.

Neo4j is basically understood to be the best tool for implementing Graph databases. Can explore more about it here: Official Neo4j Portal

What are the other sources for Graph Databases? You can go through the comparison here. https://www.g2.com/categories/graph-databases

Apart from Neo4j, you can also have Arango DB, the comparison is given here: https://www.arangodb.com/solutions/comparisons/arangodb-vs-neo4j/ and https://www.educba.com/arangodb-vs-neo4j/

Unlike the other databases, the Neo4j graph database doesn’t need to compute the relationships between the data at query time. The connections are already stored right in the database. Because of this, the queries of deeply connected data are orders of magnitude faster.

We get used to the limitations of the tools we work with, and Neo4j blows off all these limitations wide open, enabling queries never before imagined at high speeds possible.