A Case Study on Transitioning from Relational Data models to Graph Data models in an Industrial Context
Ilonen, Johanna (2023)
Ilonen, Johanna
2023
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2023041235967
https://urn.fi/URN:NBN:fi-fe2023041235967
Tiivistelmä
In this research, the graph data model challenges the well-known relational data model. The relational model, used by the relational data base, uses tables to present data and data relationships. The graph data model, used by the graph data base, explains the data as a connected graph. This fundamental structure makes the relational data model less dynamic and intuitive than the graph data model.
In our experimental setup, the graph data model is more dynamic than the relational data model. An interesting finding is that when mapping the data model needs through data analysis, it is easier to build the relational data model than the graph data model. Building a well-functioning graph data model requires understanding on how the business stakeholders describe the problem and what type and questions they want to answer based on the data. To achieve the dynamic capability of a graph data model, the data modeler needs a mindset change from an analytical approach to a social one. The inputs from the business stakeholders are the key to success in graph data modeling.
A company considering a change from a relational data base to a graph data base shall not follow hypes. Careful consideration and analysis are needed. The study shall show a clear indication of immediate and remarkable practical benefits in areas like query performance, flexibility, and agility.
In our experimental setup, the graph data model is more dynamic than the relational data model. An interesting finding is that when mapping the data model needs through data analysis, it is easier to build the relational data model than the graph data model. Building a well-functioning graph data model requires understanding on how the business stakeholders describe the problem and what type and questions they want to answer based on the data. To achieve the dynamic capability of a graph data model, the data modeler needs a mindset change from an analytical approach to a social one. The inputs from the business stakeholders are the key to success in graph data modeling.
A company considering a change from a relational data base to a graph data base shall not follow hypes. Careful consideration and analysis are needed. The study shall show a clear indication of immediate and remarkable practical benefits in areas like query performance, flexibility, and agility.