Amazon RDS vs Google BigQuery

• It's quite easy to set up and provision new RDS instances.
• Setting up multi-zone failover tolerant databases is literally just a couple mouse clicks.
• Databases are automatically backed up and have minor version upgrades applied.
• Set and forget.

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• BigQuery integrates exceptionally well with Google Storage. All you have to do is push a CSV to Google Storage, and add it to BQ. BQ will try to detect the schema and import the CSV as a table. The process is very quick.
• There are lots of ways to interact with BQ. Besides the web interface, there are also SDKs you can use to interface with bigquery from your tools. Meaning, it's not just data stuck in the cloud.
• BigQuery lets you search extremely large datasets, quickly. We have many 100m+ datasets loaded, and searching any number of fields through them is not only easy (SQL!) but fast as well (most queries finish < 30 seconds). It's not a real-time system, but for OLAP, it's unbeatable.

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• Currently RDS does not offer a No-SQL DB management through RDS. They have their DynamoDB offering for No-SQL, but I wish RDS offers popular No-SQL DB's like MongoDB in their offerings.
• RDS does not provide access to the Virtual Machine. It provides access to the database instance but not to the VM that Database resides in. This is kind of a nice to have as it would allow for fine grained performance tuning of the DB.
• Unifying RDS security by combining instance security model with AWS IAM model. Currently to manage an RDS instance you have to have two security models in place, one, to secure access to RDS through IAM and the other to secure access to the Database through the Database's own security.

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• Though it is SQL some syntax are different but they are getting used to after you use for some time.
• The legacy SQL is in beta state but can be used and you can run the query with simple SQL.
• More documentation is needed for using User-defined functions in Big Query.

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• Much less time spent managing database versions, restores, cloning and uptime.
• Allows us to create server clusters which can push our business goals.
• Allows us to push the envelope of what's possible in our backend systems.

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• Generating remarketing audience from Google Analytics data.
• Reducing the cost for storage infrastructure
• Comparability with different data sources and more depth analysis and efficient decision making

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