Well, that's a graph database. In 2000, a company named Neo Technologies created a graph database. According to the CEO, Emil Eifrem - creator of Neo4j - the major challenge they faced during the early years of graph database was marketing.
Nevertheless, the database Eifrem created is literally lit! It uses a graph structure for semantic queries. But nodes, edges, and properties to store data. Now, in this database, the graph is crucial. It also relates data items to nodes and edges - relationships between nodes are referred to as edges.
Additionally, it is also called NoSQL. And like NoSQL, it lacks schemas. The model is also easy to change and flexible. Now, a graph database is somewhat similar to a network model database. How? Well, both have general graphs.
But how does a graph database work? Well, both data and relationships between data are treated the same way. In fact, the related nodes are connected. This connection is also considered data.
What's the upside of this? Well, modeling data this way allows people to question or query the relationships in the same way they question the data. Further, rather than calculating the connection steps, a graph database reads the relationship from storage. It's also quick and responsive when it comes to complex queries.
Now, where does a graph database work best? Well, where there's a need for master data management and regulation compliances, it works the best. This is because there's a lot of data, and since a graph database creates a graph, it becomes easy to track data. It's also effective in monitoring and managing digital documents like contracts, etc.
Also, the relationships between the graphs are clear and detailed. The queries of the database provide real-time outputs. Moreover, the more relationships there are in a graph database, the faster its speed. It's also the best and most powerful database to use when there's interconnected data.
It can support queries while giving real-time data updates. However, the downside is there isn't a specific query language in graph databases. Whatever platform is used, the language of that platform is employed. It's also not the best database for transactional-based systems. In fact, for a transactional system, a relational database is more suitable. This is because it has tables to efficiently store data and perform transactions.
Another con of graph databases is their small user base. Since not many people use it because they are quite expensive, support is minimal.
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