when you first step into data engineering, the sheer number of database options can be overwhelming. i spend a lot of my time working in snowflake, but it is definitely not the only tool in the shed. to build a solid data platform, you have to understand where your data comes from and how different systems handle it. i want to break down how snowflake compares to two other popular systems you will encounter often: relational engines such as amazon rds and nosql patterns such as amazon dynamodb. this is a basic guide written from a data engineer's perspective. i will point out the similarities, the differences, and when you should use each one. the contenders before we compare them, let us define what we are looking at: snowflake : a cloud-native data warehouse built for analytics (olap - online analytical processing). storage and compute are separate, so you pay for each part independently amazon rds : a managed relational database service that runs engines like postgresql, mysql, sql server, and oracle.…