{"id":3879,"date":"2021-12-29T17:57:11","date_gmt":"2021-12-29T17:57:11","guid":{"rendered":"https:\/\/www.biconnector.com\/blog\/?p=3879"},"modified":"2021-12-29T17:57:12","modified_gmt":"2021-12-29T17:57:12","slug":"knowledge-graph-vs-relational-database-differences","status":"publish","type":"post","link":"https:\/\/www.biconnector.com\/blog\/knowledge-graph-vs-relational-database-differences\/","title":{"rendered":"Knowledge Graph vs Relational Database:
How Do They Differ?"},"content":{"rendered":"\n
Data management has evolved a lot, with the introduction of new technologies and concepts like data lakes, data vaults, graph databases, etc. <\/p>\n\n\n\n
For example, Graph databases, though just around a decade old, are witnessing a wide adoption in recent years, in the insight-hungry business world.<\/p>\n\n\n\n
However, the relational databases withstood the test of time, and are here to stay at least for the foreseeable future, even if not forever.<\/p>\n\n\n\n
In this blog post, we\u2019ll see how the worlds of Knowledge Graphs and Relational Databases differ from each other, though none of them is a replacement for the other.<\/p>\n\n\n\n
Here\u2019s a quick snapshot of the differences between Knowledge Graph and Relational Database:<\/p>\n\n\n\n In Knowledge Graphs, the data is stored as Entities and Relationships. Technically, they are called Nodes and Edges respectively.<\/p>\n\n\n\n For example, consider the information – BI Connector <\/strong>is certified by Power BI and Tableau <\/strong>for connecting to OBIEE\/OAC<\/strong>.<\/p>\n\n\n\n Here\u2019s the graph representing that information:<\/p>\n\n\n\nFactor<\/strong><\/td> Knowledge Graph<\/strong><\/td> Relational Database<\/strong><\/td><\/tr> Storage approach<\/td> Entities and Relationships are stored as Nodes and Edges respectively<\/td> 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.<\/td><\/tr>Schema<\/td> Schema-free. Unstructured.<\/td> Rigid schema. Data Structure and format are pre-defined.<\/td><\/tr> Purpose<\/td> Solely for uncovering hidden insights. Doesn\u2019t serve operational purposes.<\/td> Serves both operational and analytics purposes<\/td><\/tr> Performance<\/td> Blazing fast even for large sets of data<\/td> Relatively slower than Knowledge Graphs<\/td><\/tr> Maintenance<\/td> A lot easy, as they are schema-free<\/td> Difficult and often cumbersome, as minor changes could affect the entire structure <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Storage approach<\/h3>\n\n\n\n