Data lineage, which involves mapping dependencies between data entities, is inherently better represented using graph databases rather than traditional relational databases due to their ability to handle connections as first-class citizens. Relational databases often struggle with the complexity and inefficiency of join operations needed to track data dependencies, whereas graph databases simplify and accelerate these processes with direct node and relationship representations. This makes graph databases more suitable for tasks like impact analysis, where changes in data sources need to be tracked through complex dependency chains. Graph databases not only enhance performance but also reduce code complexity and facilitate easier visualization of data landscapes, making them ideal for extracting actionable insights from large-scale data systems. Tools such as Memgraph's Orb graph visualization library leverage the visual nature of graph analytics, allowing users to intuitively understand and interact with data lineages. Overall, the use of graph databases for data lineage projects results in faster database operations and lower code complexity, providing significant advantages over traditional relational database approaches.