Amazon RedShift happy user
Overall Satisfaction with Amazon Redshift
Amazon RedShift is used primarily by our in house data analytics and psychometric department. When we process the results of physician board certification exams we have a workflow which integrates the newly processed scoring data into RedShift which is used for analytical purposes. The data is denormalized and stored in a fashion which makes it overall more optimal for querying purposes. This data serves as the basis for final scoring calculations for a given exam. The data analytics department is then able to run their statistical analysis against the housed sets of data to ultimately determine the final test scores. We also house an "item bank" of exam questions from past exams which are used as the basis for future exams. Scoring is correlated to the item bank to help determine which questions performed well and which ones exam takers had difficulty with. RedShift is a great tool for our IT department because it helps share the responsibility of having to secure such sensitive data. Amazon is a company which we feel very confident with and RedShift has proven to be extremely robust and fast. It is nice to be able to so easily perform backups of data and rest more assured that it is in a safe form and not something which our own IT department has to manage along with all of our internally hosted transactional databases.
Pros
- Extremely fast querying allowing for concurrent analysis.
- PostgreSQL syntax which allows for developers with a SQL background to easily begin working with the data.
- Multiple output formats including JSON.
- Safe, easy, and reliable backups.
Cons
- SQL syntax support is not 100% which can lead to frustrating situations when developing a query.
- No support for database keys.
- No stored procedure support.
- It allows for an almost seamless integration of our data which can then be used by other departments for analytical purposes.
- No in house resources are needed for keeping the data alive and performing backup/migration tasks of the data in its end state.
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