GRAPHdatabases
Definition, overview & context
As part of our blog series on database technologies, this article deals with graph databases.
Graph databases are a special category of NoSQL databases that are based on the basic principles of mathematical graph theory. In contrast to relational databases, they use a network model to display data, which enables the intuitive recording of complex relationships. In this context, they offer special query languages such as SPARQL or GraphQL to search complex patterns and determine the shortest path between nodes.
These properties make them particularly suitable for applications that require highly networked data structures, such as social networks.
DEVELOPMENT AND BACKGROUND OF GRAPH DATABASES
Graph databases have their origins in the 1960s, when the first theoretical models of graph theory were developed. However, only in recent decades, with the advent of big data and the need for complex data analysis, have they found widespread use and become an important tool in database technology [1][2][4].
Graph databases are a special form of NoSQL databases that are based on the principles of mathematical graph theory. In contrast to relational databases, they represent data as a network of entities and relationships, which enables intuitive modeling of complex relationships. This structure not only allows efficient modeling of real scenarios, but also leads to improved performance and flexibility [1].
In addition, graph databases offer special query languages such as SPARQL or GraphQL. These allow complex patterns to be queried, graphs to be traversed and the shortest path between two nodes to be determined [2]. Typical applications for graph databases include the storage and analysis of connections and relationships, for example in social networks [3]. Graph databases are characterized by better performance and flexibility, as they can store relationships efficiently and execute complex database queries at high speed [1][3]. They are particularly suitable for managing highly networked data and offer advantages such as improved scalability and simplified development work [4][5].
STRUCTURE OF GRAPH DATABASES
In the underlying architecture of a graph database, data objects are represented as nodes and the relationships between them as edges. Each node can have individual properties, while edges can optionally have attributes. This structure enables an adaptable and powerful mapping of complex relationships in the form of a network [1].
Graph databases use the storage of information in the form of graphs consisting of nodes and edges to visualize complex relationships. In contrast to relational databases, which are based on rigid table structures, graph databases offer improved performance and flexibility by storing data as a network of entities and relationships. They are particularly effective for highly interconnected data sets and applications that focus on capturing and analyzing connections, such as social networks, recommendation systems, knowledge graphs and master data management [1][6]. The ability to efficiently model and map dynamic and multi-layered relationships makes it a valuable tool in modern data analysis and processing [6].
USE CASES OF GRAPH DATABASES
The use cases of graph databases are diverse and cover various areas such as fraud detection, route optimization, pattern recognition and knowledge management. Especially in the real-time processing of financial transactions, graph databases enable fast queries of relationships between data. In the area of route optimization, they help to carry out more efficient analyses of potential destination routes in order to find optimal solutions for scenarios such as selecting the shortest route or the best employee. Graph databases also play a crucial role in distinguishing and differentiating bot accounts in social media and networks. They also actively support knowledge management by enabling optimized data integration, the linking of information and the creation of knowledge graphs [1].
Another important use case is master data management, where graph databases help to manage and visualize complex hierarchies and dependencies between data objects. This leads to improved data quality and consistent provision of information [6]. In addition, graph databases are used in recommendation systems to generate personalized suggestions by analyzing user preferences and behavioral patterns and making appropriate connections [6].
ADVANTAGES
Graph databases offer various advantages, especially with regard to the storage and retrieval of highly networked data. A key advantage lies in the efficient representation and processing of complex relationships. Social networks, recommendation systems and knowledge graphs, for example, can be modeled effectively. Another advantage is the flexibility to add new relationship types without having to change the existing structure. This enables agile data modeling. In addition, graph databases are particularly suitable for analyzing network topologies, such as searching for the shortest paths or influential nodes in a network [7][8][9][10][11].
Other applications include the tourism sector and the digital humanities. Thüringer Tourismus GmbH, for example, has introduced the first graph-based database in German tourism to manage and use tourist information [10]. In the digital humanities, graph technologies are used to tokenize, lemmatize and normalize texts before they are imported into a graph database [8].
DISADVANTAGES
However, there are also some disadvantages to consider with graph databases. A key point is their complexity compared to other database types, as they require special query languages and modeling approaches [13]. Another aspect concerns the performance of certain queries, especially if no corresponding indices are available. Modern databases support administrators well, but rapidly growing indexes can make queries confusing [12]. An additional disadvantage is the need for a specialized hardware and software environment, which can lead to higher operating costs [14].
Despite their numerous advantages, graph databases are not the best choice for all use cases, as other NoSQL database types or relational databases may be more suitable in some scenarios [14]. Nevertheless, graph databases are used in various application areas due to their ability to efficiently model and query complex relationships.
TRENDS, OPPORTUNITIES AND CHALLENGES
Graph databases are becoming increasingly important in the modern technology landscape and offer promising opportunities for companies. One of the most important trends is the establishment of the property graph model, which is becoming the standard for graph databases as it represents data as nodes and relationships [16]. Another significant trend is cloud migration, where graph databases are increasingly moving to the cloud to enable easier deployment and scaling for developers and IT teams [16]. In addition, graph data science is becoming increasingly important for AI and ML, as graph databases are used as knowledge repositories for generative AI models such as RAG (Retrieval-Augmented Generation) to avoid hallucinations and incorporate company-specific data [15].
The opportunities offered by graph databases include more efficient management and analysis of highly networked data compared to relational databases [15][18]. This efficiency leads to faster decision-making throughout the company through the visualization of data relationships [15]. In addition, graph databases provide unique insights by mapping complex relationships between concepts, people and events, enabling better understanding and more efficient information search [17]. An additional advantage is that they support innovation processes by enabling companies to react more quickly to changes and new requirements [18].
However, there are also some challenges to consider. Graph databases often require the use of complex query languages, which can be a challenge for developers [17]. Another obstacle is the higher memory requirement that arises from the storage of relationships between data [17]. In addition, performance losses can occur with large amounts of data, as graph databases are often designed for a single-server architecture [4]. Another issue is the expertise required to effectively implement and maintain graph databases, which can lead to higher training costs [17].
Overall, graph databases offer numerous possibilities for mastering the challenges of data analysis in today’s networked world. Companies that use this technology effectively can benefit from more efficient data management, faster decision-making and unique insights [15][18][4].
SOURCES
[1] What is a graph database? (2023). Amazon Web Services, Inc.
https://aws.amazon.com/de/nosql/graph/
[2] Graph database. (2023). Wikimedia Foundation, Inc.
https://de.wikipedia.org/wiki/Graphdatenbank
[3] Luber, Stefan & Litzel, Nico. What is a graph database? (2019). Vogel IT-Medien GmbH.
https://www.bigdata-insider.de/was-ist-eine-graphdatenbank-a-788834/
[4] Graph database explained. (2019). IONOS SE.
https://www.ionos.de/digitalguide/hosting/hosting-technik/graphdatenbank/
[5] chrissikraus. Understanding of NoSQL databases and their types. (2023). Vogel Communications Group.
https://www.dev-insider.de/verstaendnis-von-nosql-datenbanken-und-ihre-typen-a-829d8929dd8e42fafa2ad8eb2cfde5f4/
[6] Korolov, Maria. Three use cases for graph databases. (2020). Computer Weekly.
https://www.computerweekly.com/de/feature/Drei-Anwendungsfaelle-fuer-Graphdatenbanken
[7] Efer, Thomas. Graph databases for the text-oriented e-Humanities: Alternative text representation for novel research systems and text mining methods. (2017). Self-published.
https://e-humanities.textgraph.science/book.pdf
[8] Kuczera, Andreas. Graph Technologies in the Digital Humanities: Modeling – Import – Analysis. (n.d.).
https://zenodo.org/record/3856083/files/Kuczera_Graphentechnologien20in20den20Digitalen20Geisteswissenschaften-2019-07-19.pdf
[9] Krempel, Rasmus. Networks, maps, mazes: graph-based explorative approaches to data analysis and application development in the humanities. (2016). University of Cologne. https://kups.ub.uni-koeln.de/7533/1/Diss_Final_Pub.pdf
[10] Sommer, Prof. Dr. Guido et al. Open Data creates a spirit of optimism for digitization in tourism: Results of the 3rd Round Table Open Data with outlook and recommendations for using the opportunities of an open data infrastructure in tourism. (2020). https://opus4.kobv.de/opus4-hs-kempten/frontdoor/deliver/index/docId/622/file/Open_Data_Digitalisierung_Tourismus.pdf
[11] Schuk, Vitali. Graph database model for infrastructure datasets. (2023).University of Stuttgart. https://elib.uni-stuttgart.de/bitstream/11682/13763/1/DISS_FINAL_komplett_18102023.pdf
[12] Jastram, Michael. Are graph databases better? (2021). Formal Mind Ltd. https://www.se-trends.de/sind-graphdatenbanken-besser/
[13] Everything you need to know about graph databases. (2023). Swiss Engineering Holding GmbH.
https://remotescout24.com/de/blog/613-alles-ueber-graphdatenbanken
[14] brueck. Graph databases. (2015). Technical University of Central Hesse. https://wi-wiki.de/doku.php?id=bigdata%3Agraphdb
[15] Kohlwey, Elena & Bauer, Matthias. Unique insights through graph databases. (2024). X-INTEGRATE Software & Consulting GmbH. https://www.x-integrate.com/details/neue-moeglichkeiten-durch-graphdatenbanken
[16] Kolmar, Stefan. Trends 2021 – Four developments in (graph) databases and data analysis. (2021). NetMediaEurope Deutschland GmbH. https://www.silicon.de/blog/trends-2021-vier-entwicklungen-bei-graphdatenbanken-und-datenanalyse
[17] Graph databases. (2024). StudySmarter GmbH. https://www.studysmarter.de/studium/informatik-studium/datenverarbeitung/graphdatenbanken/
[18] Kolmar, Stefan & Litzel, Nico. Graph databases offer these advantages. (2017). Vogel IT-Medien GmbH. https://www.bigdata-insider.de/diese-vorteile-bieten-graphdatenbanken-a-615118/
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