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Redshift review for the analytics environment
August 10, 2016

Redshift review for the analytics environment

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Amazon Redshift

It is used as the analytics data SOT (source of truth). All company data, whether from product, marketing, sales, etc., gets synced to Redshift where it can be easily analyzed by analysts. Redshift provides a good tradeoff between the ability to store a lot of data and perform quick and flexible queries on it.
  • Flexible, OLAP queries.
  • Fast query time (in the order of seconds for most).
  • Standard SQL language.
  • Fast ways to insert more data.
  • VACUUM is a pain, its unclear exactly how often it needs to be done.
  • Redshift has a limit on how many concurrent writes and reads you can do that won't scale to 100s of people using it.
  • Redshift lacks some Postgres queries that make some standard SQL operations hard.
  • Allowed us to easily analyze business operations and facilitate A/B testing.
  • Allowed us to quickly answer complex questions about the company's data.
  • Took a lot of work to fix some issues once we got to the limits of its usability (specifically, we had too many writes).

Than Vertica: Redshift is cheaper and AWS integrated (which was a plus because the whole company was on AWS).


Than BigQuery: Redshift has a standard SQL interface, though recently I heard good things about BigQuery and would try it out again.


Than Hive: Hive is great if you are in the PB+ range, but latencies tend to be much slower than Redshift and it is not suited for ad-hoc applications.

It's well suited to be used in an analytics environment where the consumers are 1-50 analysts who need to write complex queries against the data, where total data size is in the 1TB-1000TB range, and where there's no need for data latencies less than one hour. It won't work well in the PB scale, where there are too many consumers and data producers, and for real time applications.