According to IDC, the amount of digital data created over the next five years will be greater than twice the amount of data created since the advent of digital storage. Mirroring this sentiment, Statista predicts that data creation will grow to more than 180 zettabytes by 2025, which is about 118.8 zettabytes more than in 2020. This vast, and ever-increasing, volume of data is creating havoc for organizations that need to streamline data integration, lower the cost of data storage, and power downstream analytics.
Complicating an already challenging situation is the fact that today’s enterprise data landscape is increasingly hybrid, varied, and changing. IoT-generated data, increasing volumes of unstructured data, the growing reliance on outside data sources, and rising prevalence of hybrid multi-cloud environments are forcing organizations to scrutinize how they store data. This begs the question: How can organizations utilize data and the associated intelligence to the fullest? It has also led organizations to become conflicted as to what database approach will best serve their needs – graph or relational – so let’s review both.
Dissecting the Differences Between Graph and Relational
Put simply, graph databases emphasize the relationships between data points. They are built to store and navigate relationships using nodes to store data entities, edges that are directed links between nodes that store/uncover relationships between entities, and attributes associated with a node or edge. In some graph types, properties are simply part of the nodes and edges. Advantages of the graph approach include:
- Highly Searchable: Information is easily found even in larger graphs connecting information from well over a trillion node-edge-node sets called triples.
- Relatable: The focus on meaningful relationships makes a graph data model a fundamentally powerful way to reflect knowledge and reveal unobserved patterns. The model and data are simple to understand and easy to modify or extend.
- Intuitive: A graph data structure mimics the way people naturally go about mapping associations in their knowledge and thinking about how ideas are related.
Further, and when combined with a semantic layer that is often laid over a data lake or data warehouse, the graph can enable even greater data connections, exchanges, reuse, and business understanding.
On the other hand, a relational database organizes data points with defined relationships. Most people are familiar with these options given the fact that relational databases have been on the scene since the early 1980s. Unlike a graph database, which became commercially available in the 2000s, data structures in relational databases use data tables, indexes, and views which comprise a fixed number of attributes with fixed data types in each column. Each row in the table is a record with a unique ID.
Although the name “relational database” implies a focus on relationships, tables can only reflect that the column data in a row is related, but the nature of the relationship may never be recorded, leaving users to guess what the data represents based on sometimes obscure table or column names. It defines and builds relationships into the database itself which limits its ability to examine, add, and change correlations.
Relational databases represent data in tables, and each row in the table is a record with a unique ID (the key), while the columns of the table hold attributes of the data. So, relational databases define and build relationships into the database itself by centering on the data associated to limited types of relationships. As a result, the process of reviewing, adding, or modifying relationships in a relational database is tedious and inflexible.
While relational databases can traverse relationships, doing so often requires accessing more of the underlying data – which requires more compute power – and involves longer, harder-to-follow queries. That means users need to have specific expertise in database/dataset/query. Finally, relational data models require users to create assumptions about data relationships at the outset. All too often, these models are limited because they only capture a portion of all the possible relevant relationships between data elements.
Therefore, relational databases are best suited for completely stable and static business where the data model remains unchanged such as:
- For Key/Value storage
- If the entities in the model have extremely large attributes, for example, the complete text of lengthy documents
- For datasets with limited, static relationship types
If your organization is like most others, it likely uses relational databases already and has been for some time now. However, more and more are adopting graph to scale and take advantage of its inherent flexibility that’s tough to find in other kinds of databases. Its emphasis on relationships enables exciting new ways of thinking about data and the ability to make good business sense of ever-increasing amounts of complex data.
It also may be why, according to Gartner, graph technologies will be used in 80% of data and analytics innovations by 2025 – up from 10% in 2021 – facilitating rapid decision-making across the enterprise. Graph technology is becoming increasingly recognized as an essential tool for uncovering relationships across diverse data. By intuitively modeling complex networks of real-world relationships, it’s now possible to represent knowledge in a machine-understandable format allowing data engineers and architects to incrementally add to the value that can be derived from existing data warehouses or data lakes by enabling complex queries across diverse sources.
However, if your organization is still having difficulty choosing between relational and graph, going multi-model might be the best way to accommodate and incorporate both graph and non-graph data sources. In this way, you can leverage your existing RDBMS as you and the business better understand the capabilities of graph and the relationships they uncover.