Graph database is a pictorial representation of a database which is used to model the data in a graphical form. The graphical form allows you to navigate and traverse across the entire database along specific edge types and thus creates a structured relationship. Graph databases have come to limelight because of its capabilities in use cases like fraud detection, recommendation engines and social networking.

Data entities are stored in nodes in a graph database and edges are used to store relationships between entities. A node can have any number and kind of relationships. An edge acts as a connecting link between two nodes. It always has a start node, end node, type, and direction. It is used to represent a parent-child relationship, actions, ownerships etc.

One of the finest graph databases in the market is Neo4j. From storing the data to creating connections, Neo4j delivers efficiency at an unmatched speed. Neo4j stands out among all other graph databases for multiple reasons.

Neo4j is a native graph database. The architecture of the underlying storage of a native graph is quite different from that of a non-native graph database. Storage and processing are the two prominent features that distinguishes a native graph technology from a non-native graph. In a native graph storage, the underlying structure that contains the graph data is specifically built for storing graph-like data only. Hence data is stored in the form of nodes and relationships. Whereas, the non-native graph storage is just a storage layer that is not optimized for graphs. In a native graph storage, graph data is stored in the form of files and each file contains the data for a specific part of the graph. This method of storing allows faster graph traversal across the nodes. Also, a node points to the lists of relationships, labels and properties which makes it lightweight.

Graph processing refers to processing database operations like storage and queries. In native graph processing, index-free adjacency is followed. This means that each node is used to refer the adjacent node directly and thus each node acts as a micro-index for all the nodes nearby. This method improves efficiency and query results will be faster as the database is not crushed under its own weight. This also has proved to be cost-effective.


Neo4j as a database contains building blocks like nodes, properties, relationships, labels, and data browser. Like Oracle using SQL, Neo4j uses CQL (Cypher Query Language). The language is very simple and quite like SQL in terms of its syntax. It is a declarative pattern-matching language. Neo4j is also compatible with lot of languages like Python, Java etc.


Neo4j’s another feature is its ACID compliance, which forms a firm foundation for data reliability. ACID mainly governs the reliability of the data transactions.

A – Atomicity

C – Consistency

I – Isolation

D – Durability


Neo4j implements ACID by the following:

  1. Each database operation that is accessing the index, graph or scheme should be carried out within one transaction.
  2. Data that is taken by transactions is not shielded from modifications from other transactions.
  3. Write locks are acquired manually and node locks and nodes are acquired higher isolation levels.
  4. Locks can be acquired only at node and relationship level.
  5. Detection of deadlock is created into the transaction management core.


Combining all the above-mentioned features, Neo4j is the only transactional database that provides all of them together. Native graph storage, native graph processing, graph scalability, high availability, graph clustering, graphs in the cloud, built-in ELT, integration support, CQL are the features that highlights Neo4j in the market and makes it enterprise ready. Being a pioneer in the graph market, the technology is helping connect the customers, community, and partners.

Lightning-fast reading and writing abilities provide the best performances, also making sure the data integrity is maintained. ACIS compliance also ensures predictability of relationship-based queries. The process of loading huge data into Neo4j with low memory footprint is also a cakewalk.

Few of the key customers who use Neo4j extensively include eBay, Walmart, Cisco, Citibank, Airbus, The National Geographic Society, Microsoft, IBM, Volvo etc.