1. Handling big data: For instance, a company like Spotify, which generates massive amounts of data every day from user interactions, would greatly benefit from BigQuery's ability to quickly analyze petabytes of data.

  2. Data Warehousing: If a company wants to store all of its sales data, customer behavior data, and product data in one place for analysis and reporting, BigQuery would serve as an efficient data warehouse.

  3. Speed and Performance: Let's say a financial institution needs to run complex queries on years' worth of transaction data to detect fraud patterns. BigQuery's distributed architecture could handle these complex queries quickly.

  4. Serverless infrastructure: A start-up with a small development team might prefer BigQuery because it removes the need for server maintenance and database setup, allowing them to focus more on developing their product.

  5. Integration with other Google Cloud services: A mobile app company using Firebase for their backend could benefit from BigQuery's seamless integration with Firebase for querying and analyzing their app usage data.

When Not to Use BigQuery:

  1. Small datasets: A small business that needs to store and query its monthly sales data could likely get by with a more traditional database like MySQL, which would be more cost-effective and sufficient for its needs.

  2. Frequent updates or transactions: An e-commerce site with an inventory system that constantly needs updating wouldn't be a good fit for BigQuery. Instead, a traditional OLTP database like PostgreSQL would be a better choice.

  3. Real-time analysis: For example, a gaming company that needs to analyze player actions in real-time to adjust game dynamics would be better served by a real-time database like Firestore or a real-time analytics platform.

  4. Sensitive data: A healthcare organization storing personally identifiable information (PII) and protected health information (PHI) might prefer to use a HIPAA compliant database solution rather than BigQuery.

  5. Complex joins and operations: For example, a small online forum might need to frequently join user data with post data and comment data. While this is possible in BigQuery, it might be more efficient and cost-effective in a traditional SQL database like PostgreSQL.