Join the Dots: Data Lineage Is a Graph Problem. Here’s Why!

Data lineage, which maps dependencies between data entities, presents challenges for relational databases due to their cumbersome handling of dependencies, making graph databases a more efficient alternative. Graph databases treat data connections as first-class entities, allowing for faster operations with lower complexity, particularly beneficial in impact analysis applications. Unlike relational databases, which require complex join operations, graph databases can traverse connections in constant time (O(1)), speeding up data processing and making queries easier to write and maintain. Graph databases also offer superior visualization capabilities, enabling clearer insights from complex data landscapes, which is crucial for organizations managing large volumes of interdependent data. With tools like Memgraph's Orb graph visualization library, users can easily visualize and interact with data lineage, leveraging the inherent visual nature of graph analytics. As a result, graph databases are recommended for data lineage projects due to their speed, reduced code complexity, and enhanced visualization, providing a compelling case for their adoption over traditional relational databases.