In the Data Vault methodology, data transformations play a crucial role in preparing and loading data into the Data Vault architecture. The primary objective of data transformations in Data Vault is to ensure that the data is standardized, cleansed, and integrated before it is loaded into the Data Vault model. Here are some common data transformations used in the Data Vault methodology:

1. Data Extraction: Data extraction involves retrieving data from various source systems, such as databases, files, APIs, or other data repositories. The extraction process may include filtering, selecting specific columns, or applying other criteria to fetch the required data.
2. Data Cleansing: Data cleansing is the process of correcting or removing errors, inconsistencies, and inaccuracies in the source data. It includes tasks like removing duplicate records, handling missing values, standardizing formats, and resolving data quality issues.
3. Data Integration: Data integration involves combining data from multiple sources and transforming it into a unified format suitable for the Data Vault model. This step may include resolving schema differences, merging data from different systems, and mapping data to a common set of attributes.
4. Data Transformation: Data transformation encompasses the manipulation and conversion of data to meet the requirements of the Data Vault model. It includes tasks like data type conversions, data splitting or merging, data aggregation, creating derived attributes, and applying business rules or calculations.
5. Surrogate Key Assignment: Surrogate keys are unique identifiers assigned to each record in the Data Vault model. Data transformations may involve generating and assigning surrogate keys to the incoming data records to maintain referential integrity within the model.
6. Slowly Changing Dimension (SCD) Handling: SCDs are dimensions that change over time, and their historical values need to be preserved. Data transformations in Data Vault may include techniques for handling SCDs, such as Type 1 (overwrite), Type 2 (historical tracking), or Type 3 (partial history) dimension updates.
7. Data Loading: Once the data transformations are applied, the transformed data is loaded into the Data Vault model. This process typically involves inserting new records, updating existing records, and managing the historical tracking of changes.

It's worth noting that the specific data transformations and their implementation may vary depending on the tools, technologies, and practices adopted in an organization's Data Vault implementation.