Which solution allows for joining .csv data from Amazon S3 with Amazon Redshift without adding load?

Creating an external table using Amazon Redshift Spectrum is the correct solution for joining .csv data from Amazon S3 with Amazon Redshift without adding additional load.

Redshift Spectrum allows you to run queries against data stored in Amazon S3 without the need to load that data into Redshift. By defining an external table in your Redshift database that points to the .csv files stored in S3, you can directly query these files as if they were part of your Redshift database. This capability not only adds flexibility in managing your data but also optimizes resource usage, as it avoids the overhead associated with loading large datasets into Redshift for analysis.

Furthermore, by leveraging Redshift Spectrum, you can perform efficient joins between your in-cluster tables and external tables corresponding to your .csv files, allowing you to enrich your data with minimal impact on performance.

While other options involve processing or using AWS services to manipulate or analyze the data, they typically require additional steps, potential data loading, or processing time that may not be as efficient as querying through Redshift Spectrum. This makes the external table solution uniquely advantageous for the given scenario.