A database collects data or information methodically organized in the computer for quick search and retrieval whenever required. There are several types of databases, including centralized, distributed, relational, cloud, and hierarchical. A graph database is another type that carries a lot of benefits, one of which is storing and assessing relationships between data. No wonder that the application of this method technology is expected to grow in the coming years. What is graph database, types, and benefits? Read on to find out.
Definition of a graph database
As the name implies, a graph database combines two words: graph and database. To put it plainly, it is a database that uses graph structures to store data and navigate relationships. The data is also stored flexibly, which means that it is not restricted to a predefined model. In this method of storage, the relationships between data are regarded as necessary as the data itself. It shows how a particular individual is connected to the others in the model.
The difference between a graph database and others is that while the former stocks connections alongside the data, the latter calculates relationships through JOIN operations in DBMS. They are used for various purposes, including social media networks, fraud detection, identity and access management (IAM), and master data management (MDM).
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Types of GDB
There are two primary types of graph databases. These are property graphs and RDF graphs. The main focus of property graphs is on querying and analytics, while RDFs are on data integration. The vertices are shared between them (vertices is the plural of vertex, a corner where the edges meet) and “edges” (the connection between the vertices). Because of their versatility, property graphs are used in various industries, including manufacturing, finance, public safety, and retail. You can find the application of RDF graphs in pharmaceutical companies, healthcare agencies, and statistical institutes.
What are their benefits?
There is perhaps no better alternative to GDB when it comes to searching data. In a graph, each vertex maintains information about its neighboring vertices only. The data does not need to be touched or reloaded for a specific query. What that does is make tracking, analyzing, and retrieving data relatively easy, even as its size keeps increasing.
Perhaps no other type of database offers as much flexibility with a query as GDB. You have the choice of adding or removing new vertices to expand or shrink the data size. The graph is formatted in such a way that you can assess complex relationships for deeper analysis quickly.
Data updates and queries simultaneously
GDB is also a preferred data storage mechanism because it can update enormous data while handling queries simultaneously. Adding new data leads to an automatic full table scan with other databases since they are structured to run. However, adding any new data with this type of database will only alter that particular file.
Aggregation of queries is easy
The problem with the aggregation of queries in non-GDB is that the grouping of data is determined beforehand. You can change the data with the simple addition and removal of vertices. For example, the way aggregate queries function is entirely restricted on other data storage systems because of how the data is grouped.
What is a graph database, its types, and its benefits? The information mentioned above will give you an idea regarding it. Storing, organizing, updating, and understanding data while understanding the relationship leads to more significant insights and easy predictions.