3.2. Planning and Implementing Storage and Data Solutions

💡 First Principle: Google Cloud's data and storage products aren't redundant options covering the same ground — each one is optimized for a specific access pattern (transactional versus analytical, structured versus unstructured, global-scale versus single-region), and the exam's "which service" questions are testing whether you can match the access pattern described to the product built for it.

Picking the wrong data product doesn't just mean suboptimal performance — it can mean a solution that technically doesn't work at the scale described, or one that costs an order of magnitude more than necessary because it wasn't designed for the query pattern being run against it. This is precisely why so many exam scenarios pack in specific numbers (queries per second, data volume, consistency requirements) — those numbers are the clues pointing at the intended answer.

⚠️ Common Misconception: BigQuery is a general-purpose relational database comparable to Cloud SQL. In reality, BigQuery is a serverless data warehouse built for large-scale analytical (OLAP) queries, while Cloud SQL is a managed relational database for transactional (OLTP) workloads — both accept SQL, but they solve different problems.