Transforming CSV Data with AWS Athena and SQL

AWS Athena is a serverless query service that allows you to analyze data stored in Amazon S3 using standard SQL. It provides an efficient and scalable way to process large datasets without requiring any infrastructure or maintenance. In this article, we will explore how to use AWS Athena and SQL to transform a CSV file from a column-based format into multiple rows.

Understanding the Problem

The problem statement involves converting a CSV table in a specific format to another format where each value is separated by a slash (/) and can be further processed for analysis. The original CSV data contains four columns: ID, Starting Time, Ending Time, Failure Sector, and Recycling Rounds. We need to transform this data into multiple rows, with each row representing a single value from the original column.

Background

The provided SQL solution uses several AWS Athena features, including:

1. Split: A function that takes a string as input and returns an array of substrings separated by a specified delimiter.
2. Unnest: A function that takes an array as input and returns a flat table with each element from the array as a separate row.

Step-by-Step Solution

To transform the CSV data using AWS Athena and SQL, we will follow these steps:

Sample Data Creation

We create two sample datasets to demonstrate the transformation process. The first dataset contains the original CSV data, while the second dataset represents the desired output format.

-- sample data
with dataset (ID, Starting_Time, Ending_Time, Failure_Sector, Recycling_Rounds) AS
         (VALUES 
             (1, '5am/8am/1pm', '6am/10am/3pm', '/(5)/', '0/0/'),
             (2, '4am/6am/3pm/7pm', '7am/8am/5pm/11pm', '///(1)', '1//1/')
         )

-- query
select st, et, fs, rr
from dataset,
unnest (split(Starting_Time,'/'), split(Ending_Time,'/'), split(Failure_Sector,'/'), split(Recycling_Rounds,'/')) 
    as t(st, et, fs, rr);

Explanation

1. We first create two sample datasets using a with clause in the SQL query. The first dataset contains the original CSV data, while the second dataset represents the desired output format.
2. In the select statement, we use the unnest function to flatten the arrays of values from each column. This produces a flat table with multiple rows for each value from the original columns.
3. The split function is used to separate the values in each column by the slash (/) delimiter.

Output

The output of this query will be the transformed CSV data in the desired format:

Conclusion

In this article, we demonstrated how to use AWS Athena and SQL to transform a CSV file from a column-based format into multiple rows. We discussed the key features used in the solution, including split and unnest, which are essential for working with arrays and flatten data in Athena.

This technique can be applied to various data transformation scenarios, making it an efficient and scalable way to process large datasets stored in Amazon S3 using AWS Athena.

Last modified on 2024-06-23