Amazon SimpleDB vs Google BigQuery

• Flexibility
• Easy to learn and use
• AWS integration

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• BigQuery is a highly optimized, columnar oriented database, and as such it exceeds when doing complex aggregations over massive datasets, i.e. computing n-tiles, statistics, sorting, etc.
• BigQuery is seamlessly integrated with the rest of the Google Cloud Platform stack, and as such it is extremely easy to move data in and out of BigQuery for analysis and storage. However, it also exposes very well defined APIs for inserting and streaming data in, and as such can be used easily with other on-premeses or cloud solutions.
• Because BigQuery is fully managed, there is no need to think about provisioning machines, optimizing memory/cores, 'vacuuming', etc. This increases the 'democratization' effect BigQuery can have, as a basic knowledge of SQL is all that is needed to get started.

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• Non AWS environments
• Strict storage limit (but well we have DynamoDB for storage issues)

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• SQL syntax is not exactly same as ANSI SQL so there is a learning curve. Traditional SQL queries cannot execute in BigQuery which limits portabiltiy of the code.
• Limitation on visualization: We can improve visualization in data studio by bringing in the ability to support complex functions/formulas such as Tableau can do.

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• Reduced database administration time
• Reduced data model analysis time
• Lower cost of resources in projects in general

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• We were able to reduce our investment in self-hosted Postgres and move our bigger data assets to BigQuery. Overall, we saved hundreds per month, thousands per year.
• We are able to search through very large datasets with BQ that were difficult to search with standard Postgres, even on very large servers. This gave us the ability to do ad-hoc data introspection easily.
• Because we already used Google Storage, it was easy to integrate BQ into our environment.

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