Use a traditional data warehouse architecture and store the data in a structured format, as it is more suitable for handling large volumes of diverse data.

Design an ETL pipeline that ingests data in its native format, performs minimal transformations, and stores it in a data lake for further processing and analysis.

Perform extensive data cleansing and transformation before ingesting the data into the data lake, to ensure data quality and consistency.

Use a single-stage ETL process to load all data into the data lake and perform all transformations and analysis there, without considering data governance and security.