We want to use AWS Athena for analytics and segmentation, our problem is that our data is schemaless, rows are different with some similar columns.
Is it possible to create table without defining all the columns?
When we query we know the type (string/int) of each column so if there is a way to define on the query it will be great.
We can structure the data in anyway needed to support schemaless and in any format: CSV / JSON.
Is Athena an option for schemaless uses?
There are many ways to use Athena in schemaless uses and you need to give specific examples of scenarios that you want to support more efficiently as in Athena you pay based on the data that you scan and optimizing your data to minimize the data scan is critical to make it a useful tool in scale.
The simplest way to get you started as you are learning the tool, and the types of queries that you can run on your data, is to define a table with a single column ("line"), and then do the parsing of the data that you want using string functions, or JSON functions if the lines are in JSON format.
You will get good time performance if you have multiple files, but it will be expensive as you need to scan all your data for every query. I suggest that you start with these queries as a good way to define your requirements. As you see the growth of usage, start optimizing the use cases by using the CTAS (Create Table As Select) commands that will generate parquet versions of the original raw data to support the more popular (and expensive) use cases.
You are welcome to read my blog post that is describing the strategy and tactics of a cloud environment using Athena and the other AWS tools around it.
