Data lineage, essential for tracking the flow and transformation of data across platforms, poses significant challenges due to the diversity of systems, increasing query complexity, and the substantial effort required for setup and maintenance. Automated data lineage solutions, particularly for cloud data warehouses like Snowflake and BigQuery, have become more accessible, leveraging SQL parsing and query logs. However, these solutions often struggle with coverage beyond warehouses, including BI tools and upstream data sources. Spark-based Lakehouses present additional difficulties due to the complexity of parsing languages like Scala, Python, and Java. Source-code-based data lineage, facilitated by tools like dbt and Databricks, offers advantages such as minimal lag by directly parsing code repositories hosted on platforms like GitHub. The OpenLineage standard enhances interoperability between data tools by standardizing lineage information exchange, despite requiring code changes and adoption within organizations. Effective evaluation of data lineage solutions should consider coverage, setup and maintenance effort, upstream pipeline coverage, and actionable use cases to improve data trust and operational efficiency.