One of the technologies that have come to the fore with the rapid rise of the big data concept in the last ten years is graph databases.
With the increasing data volume, the effect of graph databases for analyzing these data and revealing the relationships between them is frequently discussed. Graph technology is encountered even in our daily life. For example, along with social media platforms, suggestions such as “people you may know” and “you may also like” are some of the reflections of graph databases.
A graph is formed by nodes that represent the entities and edges or links that represent the relationship between these entities. On the other hand, graph analytics is based on a model that represents entities individually and the analysis of various types of connections between these entities.
Traditional methods are no longer sufficient to reveal the relationships between data. As a matter of fact, they can no longer provide solutions for many issues. At this point, graph analysis comes into play. Gartner mentions that graph technology forms the basis of modern data analytics with its capabilities to develop and improve user collaboration, machine learning models, and explainable artificial intelligence. Besides, Gartner states that half of the questions of its customers about artificial intelligence are related to the use of graph technology. This percentage is really high when we consider the intensity of the activities in artificial intelligence. Data analysts rely on graph technology for fast solutions to complex business problems. Therefore, according to Gartner’s research, it is predicted that the usage rate of graph technologies will increase from 10% to 80% from 2021 to 2025.
So, what are the differences between graph databases and classical databases? We mentioned about graph databases keep data as node and edge by taking advantage of graph theory in mathematics. Thus, complex calculations, which normally take a long time, are solved much faster with a lower budget by scanning the neighborhood matrices instead of connection searches on relational databases. To explain in more detail, operations that are almost impossible to perform over relational databases such as finding the shortest path between two nodes, centrality analysis and traverse operations can be completed in less than seconds on graph databases.
As we mentioned above, many sources guiding the industry stated that the concept of graph analytics will be the most important analytic trend in the coming years. Data owners realize that the analyses with classical methods are insufficient because of the increase in the data volume they own. Therefore, they start to complete these deficiencies with the graph approach and this situation also increases the importance of this approach.
Are all types of data suitable for graph analysis? What types of analysis can be done with this technology?
One of the most important reasons that make graph technologies more popular is, graph models have a higher power to depict real-life events. Both the query language approach (gremlin, cypher, DQL) and the visualization phase of the data create an intense narrative difference at this point. While traditional relational databases separate data through tables physically, graph databases perform this logically. Thus, anyone who has data with logical connections takes advantage of graph analytics. Thanks to data mining techniques, analysts can use graph technology easily without the need for an IT expert. Accordingly, processes can progress much faster. Graph technologies produce very effective solutions especially in crime analytics, network analysis, and monitoring various money movements. The investigation, public security, and banking sectors are now using graph technology actively.
Can graph analytics be performed with the data kept in relational databases for many years?
Unfortunately, this is a difficult point for institutions, because most of the products in the market direct you to transfer your data to a graph database. At this point, IT decision-makers consider the cost of moving big data and ETL processes. They also stand off the new data source that they have to feed constantly. The most known products in the industry try to direct people to their products by positioning themselves as a complete replacement of relational databases with ACID support. However, such promises do not convince IT professionals.
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