Scenario:The ML engineer needs a low-overhead solution to query thousands of existing and new CSV objects stored in Amazon S3 based on a transaction date.

Why Athena?

Serverless:Amazon Athena is a serverless query service that allows direct querying of data stored in S3 using standard SQL, reducing operational overhead.

Ease of Use:By using the CTAS statement, the engineer can create a table with optimized partitions based on the transaction date. Partitioning improves query performance and minimizes costs by scanning only relevant data.

Low Operational Overhead:No need to manage or provision additional infrastructure. Athena integrates seamlessly with S3, and CTAS simplifies table creation and optimization.

Steps to Implement:

Organize Data in S3:Store CSV files in a bucket in a consistent format and directory structure if possible.

Configure Athena:Use the AWS Management Console or Athena CLI to set up Athena to point to the S3 bucket.

Run CTAS Statement:

CREATE TABLE processed_data

WITH (

format = 'PARQUET',

external_location = 's3://processed-bucket/',

partitioned_by = ARRAY['transaction_date']

) AS

SELECT *

FROM input_data;

This creates a new table with data partitioned by transaction date.

Query the Data:Use standard SQL queries to fetch data based on the transaction date.

References:

Amazon Athena CTAS Documentation

Partitioning Data in Athena