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Advantages of data analysis with graph databases

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Article from ADMIN 58/2020
Analyze scattered but related data in real time with a graph database.

The volume and variety of collected data is constantly growing, which prompts enterprises, for certain use cases, to turn to a new generation of database technology. The structure and query language of graph databases allow the correlation and recognition of current data in real time. Furthermore, graph databases offer massive speed advantages when it comes to evaluating special datasets.

Limits of Relational Databases

Most enterprises, through websites and other methods, build huge databases. Analyzing these databases can reveal important information about customers, helping to predict user behavior, determine optimal pricing, and overcome operational challenges. For some time now, the hurdles for the use of graphs have been falling as a variety of functions from modern query languages have become available. On top of this, most cloud vendors now offer graph technology as a service in addition to the proven relational database management system (RDBMS) options.

RDBMSs were created in the 1970s for storing, processing, and retrieving structured data, such as records of financial transactions that are stored in a tabular format, and they are still widely used in organizations. However, data volume is now a genuine problem, and developers are increasingly looking for other approaches. NoSQL databases and key/value stores can solve part of this challenge, but they do not provide the analytical capabilities that organizations need to turn data into actionable insights.

Additionally, other fields of application centering on Big Data (e.g., fraud detection, supply chain management, risk analysis, or recommendation generation) also cause headaches because individual data structures have to be created in relation to each other. The graph database has been around for several decades as a concept, but recent innovations in storage and processing performance and the evolution toward Turing

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