Your company is designing its data lake on Google Cloud and wants to develop different ingestion pipelines to collect unstructured data from different sources. After the data is stored in Google Cloud, it will be processed in several data pipelines to build a recommendation engine for end users on the website. The structure of the data retrieved from the source systems can change at any time. The data must be stored exactly as it was retrieved for reprocessing purposes in case the data structure is incompatible with the current processing pipelines. You need to design an architecture to support the use case after you retrieve the data. What should you do?

Storing the raw, unstructured data directly in a Cloud Storage bucket is the most suitable approach. Cloud Storage offers highly scalable, durable, and cost-effective object storage, ideal for storing data in its original format, regardless of structure, for potential reprocessing. This "store everything" approach ensures data immutability and availability for future pipeline adjustments or schema changes. Processing pipelines can then easily retrieve this raw data from the bucket. Storing data in BigQuery tables (options A and B) is not ideal for raw, unstructured data that may change schema frequently. BigQuery is a columnar data warehouse optimized for structured data and analytical queries, and it would require defining a schema upon ingestion, which contradicts the requirement to store data exactly as retrieved, especially if its structure changes. Sending processed data for reprocessing (options A and C) defeats the purpose of storing raw data for reprocessing in case of incompatible structures.