A significant data migration at Lyft involved moving a petabyte-scale data warehouse from Redshift to a Hive/Spark/Trino-powered data lake, which faced many challenges such as selecting which data to migrate, converting extensive SQL scripts to a new dialect, and validating the migration for stakeholder approval, ultimately extending the process from one to over three years. This experience highlights the common difficulties faced in data migrations, which are often lengthy, costly, and manual, hindering teams from pursuing innovative analytics. In response to these challenges, Datafold has introduced significant updates to its product, offering a 3-in-1 experience to streamline the most tedious aspects of data migration. These updates include Cross-Database Diffing for fast data validation, Column-Level Lineage for optimal data prioritization or deprecation, and SQL Translation to automate SQL query rewrites. By using Datafold, teams can enhance data quality, increase speed and transparency, and simplify technical complexities during migrations.