A Comparison of Data Warehouse and Query Engines on AWS

Choosing the right data warehouse and analytics infrastructure on Amazon Web Services (AWS) can be confusing. Part of the problem is understanding the differences between the older on-premises warehouses and three generations of cloud data warehouse and query engine technologies that have all been evolving over time.

Comparison - Redshift, Athena, Snowflake, Firebolt

What follows is a side-by-side comparison of the options on AWS across 1st, 2nd, and 3rd generation data warehouses and query engines.


The detailed comparison of Redshift, Athena, Snowflake, and Firebolt across architecture, scalability, performance, use cases and cost of ownership highlights the following major differences:

In short, some data warehouses are better for different use cases. Using this information will hopefully help you choose the right data warehouse or query engine for different use cases. It may also help you prepare your analytics infrastructure so that these choices can be made. 

There has never been such a thing as a single data warehouse that satisfied all the analytics needs of a company. The combination of today’s modern data pipelines and data lakes with the simplicity of cloud services have made adding another cloud warehouse relatively straightforward and cost effective. Those companies that were already using a cloud data warehouse for reporting and dashboards were able to add Firebolt as another cloud data warehouse in weeks, and use it for ad hoc, semi-structured data, operational and customer-facing analytics, while leaving the existing analytics in place.

If you do put a modern data pipeline in place that does make it easier to redirect your data from a data lake or other “single source of the truth” then you will be able to choose the best combination of price and performance for each analytics need.

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