Don’t understand the premise of the question and how the current solution runs

[or-3]

Question: “A company uses Amazon Redshift as its primary data warehouse for its e-commerce application. They added a new report written in SQL to the sales table. This report must compare specific columns of this year’s sales data with the previous years. But, they keep data older than 1 year in an Amazon S3 bucket. This report is running slowly, causing bad performance and long waiting times. The system needs to be optimized to make these queries faster. What is the MOST efficient solution to meet these requirements?”

The answer to this question is to use Redshift Spectrum, that makes sense. But what I don’t quite understand is how does the current solution query the S3 data? In a way, it might not matter but it’s confusing nonetheless to not really know what you’re solving for.

[Nikee-TutorialsDojo]

Hello or-3,

Thank you for posting here, and apologies for the late response.

The answer to this question must be “Use the report’s SQL statement to retrieve data directly from Amazon S3. Save these results in a Redshift materialized view. Adjust the report to refresh using SQL REFRESH and then extract data from the materialized view.“ This approach enhances the efficiency of accessing frequently queried data by reducing the computational load each time the report is generated. A materialized view stores the result of a complex query and only needs to be refreshed periodically, which can significantly decrease the time it takes to run the report, thereby improving performance. Furthermore, a solution that runs SQL REFRESH on the materialized view would ensure that the latest data from the current sales table is included in the report.

We will make the necessary updates regarding this item. Thank you for understanding.

Regards,

Nikee @ Tutorials Dojo