You have a petabyte of analytics data and must support data-warehouse analytics in Google Cloud while exposing files for batch tools in other clouds. What should you do?

BigQuery is ideal for petabyte-scale data warehousing and analytics due to its serverless architecture and performance. Storing a compressed copy in Cloud Storage addresses the requirement to expose files for batch tools in other clouds. Cloud Storage is a highly durable, scalable, and cost-effective object storage service that is easily accessible from various environments. Storing the entire dataset in BigQuery alone would not allow direct file access for batch tools outside Google Cloud. Bigtable is a NoSQL wide-column database, not optimized for analytical SQL queries or data warehousing. The option of splitting warm and active data with a specific ratio is an arbitrary solution that adds complexity without directly addressing the need for file exposure to external batch tools.