What challenge is commonly faced when migrating from a data warehouse to a data lake?

Migrating from a data warehouse to a data lake often results in increased data reliability issues. This is primarily due to the different ways data is managed and structured in these environments.

In traditional data warehouses, data undergoes rigorous cleaning, transformation, and validation processes before it is loaded into the system. This ensures high-quality, reliable data that is well-structured and ready for analysis. In contrast, data lakes typically store a vast array of raw data in its native format, which may not have undergone the same level of transformation or quality checks. Consequently, users migrating to a data lake can encounter challenges related to data integrity, consistency, and reliability. There may be a mix of structured, semi-structured, and unstructured data, leading to complications in ensuring that data remains reliable and trustworthy for decision-making.

The other aspects, such as increased data retrieval speed and improved security and privacy, do not typically arise as common challenges during this migration process—indeed, optimizations may lead to better performance in some aspects. Similarly, reduced complexity in data management is not generally characteristic of data lake implementations, which can introduce new complexities due to the diversity of data formats and the necessity for new strategies in data governance and management.