Use a single-stage ETL process to load the data directly into a data lake and perform all transformations and analysis there.

Create a multi-stage ETL pipeline with intermediate data staging locations to handle the different data types and sources, and perform transformations at each stage.

Only process structured data and ignore unstructured data due to the complexity of handling different data types.

Use a traditional batch processing approach to handle the data, as it is more cost-effective than using a distributed computing framework like Apache Spark.