Data migrations, once relatively straightforward, have become increasingly complex and daunting as businesses have become more reliant on data-driven decision-making and as data volumes have exponentially grown. This complexity is further exacerbated by the advent of cloud data platforms like Snowflake, Databricks, and BigQuery, which have made data collection and transformation more accessible but introduced challenges with multi-petabyte migrations and complex dependencies. These migrations often involve costly and time-consuming processes, including manual code translations and the use of SQL translators that struggle with diverse SQL dialects. While some organizations turn to external consultants for assistance, they often face challenges with ensuring post-migration data accuracy and completeness, leaving data teams caught between the need to modernize infrastructure and the difficulties of executing these migrations. However, a significant shift in how data migrations are conducted is anticipated, promising to improve the experience and capabilities of data teams in creating data products.