You batch-load hundreds of thousands of social media posts daily, run them through Cloud Natural Language to extract topics and sentiment, store raw posts for archive/reprocessing, and create dashboards for internal and external users. How should you store raw and extracted data at lowest cost and fewest steps?

Storing raw social media posts in Cloud Storage is cost-effective for large volumes of unstructured data that needs to be archived and potentially reprocessed. Cloud Storage offers various storage classes (e.g., Coldline, Archive) for optimal cost based on access frequency. Writing the extracted, structured data into BigQuery is ideal for analytical dashboards and queries due to its serverless, petabyte-scale data warehousing capabilities and cost-effective storage for structured data. Storing both in BigQuery would be less cost-effective for the raw, unstructured posts and less flexible for reprocessing. Cloud SQL is a relational database and not suitable for storing hundreds of thousands of unstructured social media posts or for large-scale analytical queries. Feeding directly from the source to the API might not allow for archiving or reprocessing the raw posts, which is a requirement.