Amazon RDS vs Google BigQuery

• Getting the data in and out of our databases, especially with the close integration RDS has with S3 buckets.
• Less overall management of the database servers. Our DBAs are now do more DBA work than server admin work, which moves them higher on the value chain.
• Performance and Scalability.

Read full review

• Big Query is fast and based on the cloud you can run your query on a huge dataset. Huge means data in TB's. This also reduces the company cost to build that kind of infrastructure to store data.
• Not specific to Google Analytics but you can import data from different sources for analysis purpose and use the power of the cloud to run the query.
• Not much time to learn - You don't need any special skills, just SQL and you can use Big Query for your use. Learning SQL is not a big task you can learn it in a week.
• Big Query refrence schema and different sample query are available to practice on queries.
• Google also provide sample dataset to use then purchase Big Query.

Read full review

• RDS is much more expensive than MySQL+EC2.

Read full review

• BigQuery does impose quite a few limits on the higher end queries, although they are entirely understandable. For example, very large 'GROUP BY' clauses can sometimes fail with a "Resources Exceeded" error, as the distributed computational nature of BigQuery forces all of that data to be compiled on a single machine, and when that machine runs out of memory it throws the aforementioned error. You can increase your Billing Tier to complete these queries, though.
• When getting data out of BigQuery, there are also quite a few limits. For example, if you are returning a large result set, you are essentially forced to write the results to a table and then export that table to Google Cloud Storage to then be downloaded. However, during the export process, if the table is large, Google will split that table into many smaller blocks that need to be reassembled.

Read full review

• Combined with metabase, RDS has enabled numerous non-technical members of our team to run and share sophisticated queries.
• The engineering time spent on worrying about RDS has been minimal. We set it up once over a year ago and haven't touched it since.
• We're relying on metabase and RDS more for operations-related dashboards, because it's generally faster to iterate on a simple query than a complex web application.

Read full review

• If BigQuery fits your business needs (see best scenarios) it can yield great ROI.
• You maybe able to answer questions with ease which would take much more effort with other big data query technologies (HIVE, Spark, ...). You miay pay some costs.
• Following some best practices (how to construct and limit your queries) can decrease your costs.

Read full review