Relational database management systems (RDBMS) became popular among businesses in 1980’s due to their ability to conveniently store and retrieve data related to a business’ customers, employees, projects, sales etc. Though relational databases are still quite relevant, a massive growth in the amount of data generated over the last decade has provided businesses a plethora of opportunities to improve their services by shifting towards data driven business decision making. This, in turn, has led to a rise in the popularity of graph databases. As relational databases grow exponentially in terms of complexity due to complex relations between the various entities, execution of relational queries becomes a herculean task. Complex relational queries involve several join operations which can take an insanely high amount of time to run and are pretty expensive to run in real time.
Here’s where graph databases come handy. Graph databases, as the name suggests, use graph technology to deliver high performance and flexibility. Thanks to their agility, graph databases model the dynamic real world scenarios quite comfortably. Companies often have to adapt to unprecedented changes in the market and industry, and graph databases are capable of adapting to such changes much quicker than traditional RDBMS, largely due to their schema-free approach. In simpler words, a graph database allows business leaders to make decisions without worrying too much about how quickly their database will be able to adapt to those changes.
Graph databases are designed to take advantage of connected data, which is exactly where traditional RDBMS falls short. Connected data helps companies understand their customers’ requirements and preferences in great depth. As the data generated grows exponentially with time, businesses can leverage this data to get business insights and provide relevant recommendations in real time. Bigger datasets give better insights.
Large scale businesses which generate a large amount of data everyday have shifted to graph databases to customize their user experience and increase customer satisfaction. In other words, you can ‘ask’ graph databases complex questions related to just about anything about your business. The database uses graph algorithms to traverse complex relations in the data and is likely to present insights about your business which you might not even have thought of before. All this can be done in real time.
Convenient data accessibility is one of the key reasons why machine learning engineers and data scientists prefer using graph databases. Simple query languages for graph databases add to the ease of accessing data. Data analytics becomes a lot simpler when using graph databases.
Graph databases have been adopted quite widely among large scale businesses. Walmart and eBay, for example, use graph database to provide real time product recommendations to their customers. Graph databases enable tracking of complex supply chain networks. In conclusion, we can say that with the rise of big data, several large scale businesses have switched to graph databases, considering the massive benefits which they provide over relational databases.