____________ is ideal for data lakes where transformations on data are applied before raw data is loaded into the data lake.

The ETL (Extract-Transform-Load) Process is specifically designed for scenarios where data is extracted from various sources, transformed into a suitable format, and then loaded into a storage system such as a data lake. This method emphasizes the transformation of data prior to its arrival in the data lake, which allows for cleaner, more structured datasets that are easier to analyze later.

In the context of a data lake, using the ETL process ensures that raw data is processed and refined according to the needs of the analysis before it is stored. This can include operations like data cleaning, normalization, enrichment, or aggregation, which enhance data quality and usability for downstream analytics.

Other methods, while they serve important roles in data management, do not fit as neatly into the requirement of applying transformations before loading into a data lake. For instance, data pipelines can encompass various workflows but might not always follow the traditional ETL cadence of transforming data before it is loaded. Stream processing focuses on real-time data flows rather than preloading transformations, and batch processing typically handles large volumes of data at once, often after the data has been loaded, rather than before. Thus, the ETL process is the most suitable choice for this scenario.