Knowledge Graph vs Relational Database:
How Do They Differ?
Data management has evolved a lot, with the introduction of new technologies and concepts like data lakes, data vaults, graph databases, etc.
For example, Graph databases, though just around a decade old, are witnessing a wide adoption in recent years, in the insight-hungry business world.
However, the relational databases withstood the test of time, and are here to stay at least for the foreseeable future, even if not forever.
In this blog post, we’ll see how the worlds of Knowledge Graphs and Relational Databases differ from each other, though none of them is a replacement for the other.
Differences Between Knowledge Graph and Relational Database
Here’s a quick snapshot of the differences between Knowledge Graph and Relational Database:
|Factor||Knowledge Graph||Relational Database|
|Storage approach||Entities and Relationships are stored as Nodes and Edges respectively||Data is stored in tables as rows and columns. Joins are created between tables for fast querying.|
The relationships between the columns of a table are inferred, but never stored separately.
|Schema||Schema-free. Unstructured.||Rigid schema. Data Structure and format are pre-defined.|
|Purpose||Solely for uncovering hidden insights. Doesn’t serve operational purposes.||Serves both operational and analytics purposes|
|Performance||Blazing fast even for large sets of data||Relatively slower than Knowledge Graphs|
|Maintenance||A lot easy, as they are schema-free||Difficult and often cumbersome, as minor changes could affect the entire structure|
In Knowledge Graphs, the data is stored as Entities and Relationships. Technically, they are called Nodes and Edges respectively.
For example, consider the information – BI Connector is certified by Power BI and Tableau for connecting to OBIEE/OAC.
Here’s the graph representing that information:
From the above graph, we can recognize the information is stored in 4 Nodes and 2 edges.
The nodes are –
- BI Connector
- Power BI
And the Edges are –
- is certified by
- for connecting to
The major advantage of Knowledge Graphs over relational databases is it stores the relationships as well.
The storage approach of relational databases is a lot different. The relational databases store data in tables as rows and columns.
Each table is connected to another table by a common data point, for faster querying and efficient storage. The relationships between the columns of a table are not stored, unlike in the knowledge graphs.
Knowledge graphs are schema-free. You can add as much information as you want for each entity, based on the information available for each of them. There’s no compulsion to stick to a specific structure.
In the case of Knowledge Graphs, no available information will be lost due to its schema-free features.
The relational databases, on the contrary, are schema-driven. You define a specific, standard data structure and format (for every data point in each table) even before data is saved in a relational database.
Organizations struggle to store and manage certain available information in relational databases, as they have a rigid schema.
Graph databases can run queries much fast even for large datasets. The performance advantage, coupled with the schema-free features, has made many organizations tap into Graph databases for deriving data insights for applications such as fraud detection, national defense, and social media recommendation engines, etc.
In the case of relational databases, the query performance is optimizable by minimizing the dataset and using efficient schemas. However, they cannot still match the query performance of Graph databases for a dataset of the same size.
Knowledge graphs are used solely for deriving insights. The graphs are of little to no help from an operational standpoint. Hence, it’s impossible to replace a relational database with a graph database.
When your business is insight hungry, you can choose a graph database for uncovering insights that could otherwise stay hidden forever if you choose to stick with the relational database.
The relational databases, on the other hand, serve both operational and analytical purposes. From an operational perspective, they cannot be replaced. The ability to store data in the form of rows and columns is essential for running a business successfully.
The relational databases are heavily utilized by many organizations for analytical purposes as well. The data visualization tools such as Power BI and Tableau help these organizations still uncover a ton of insights that impact critical business decisions.
As Knowledge Graphs are schema-free it’s easy to add as many data points as you like whenever you want.
The maintenance of a graph database is worry-free because you don’t have to think about how the additional data that is stored in the graph will affect the existing data.
However, it’s the exact opposite for a relational database. The maintenance of a relational database is extremely difficult, as you’ll have to analyze the entire architecture before making even a minor change to the structure.
When we look at the differences, it’s evident that a relational database and a graph database are not alternatives for each other.
Instead, they both serve different purposes. In fact, most businesses today have a relational database for day-to-day operations. And they are also making the most of it for analytics with necessary tuning for query performance.
However, if you’d like to derive more insights based on the relationships between various entities (at blazing-fast query performance), it’s time to go for a graph database, that runs parallely with a relational databse.