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Asked: 2024-09-26T00:45:47+05:30 In:

Is it possible to remove specific rows of data from tables in Athena?

I’ve been diving into using AWS Athena for some data analysis projects, and I hit a bit of a snag that I could really use some help with. So, here’s my question: Is it possible to remove specific rows of data from tables in Athena?

I’ve come across some interesting datasets, and while I’m able to run queries and get results, sometimes I find that I want to exclude certain rows based on specific criteria. For instance, I have a table with user activity logs, and I want to remove entries from users who haven’t been active in the last year. I know Athena is built on Presto and is great for querying data, but I’m not too clear on the best way to “delete” rows, if that’s even possible.

What’s frustrating is that I’ve seen ways to select data and make new tables, but I’m not necessarily looking to create new tables each time I want to analyze a subset of data. I just want to refine what I’m working with on the fly without having to duplicate everything. Also, I assume that with Athena being a serverless query service, there might be limitations compared to traditional SQL databases where you can just run a DELETE command, right?

Some people have suggested using CTAS (Create Table As Select) for filtering the data, which sounds like a workaround, but it feels a bit cumbersome if I need to do it frequently. Plus, it leads to more tables cluttering up my S3 bucket, which isn’t ideal. Does anyone have experience with this kind of thing? Are there better approaches that you’ve found?

I’d love to hear how you all handle similar situations. What strategies do you use in Athena to manage or exclude specific rows from your analysis? Any tips or insights would be super helpful. Thanks!

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    2 Answers



    1. AWS Athena Row Deletion Question

      Hey there!

      So, I get where you’re coming from! Working with AWS Athena can be a bit tricky when it comes to managing data. Unlike traditional SQL databases, you’re correct that Athena doesn’t allow for a straightforward DELETE command since it’s primarily designed for querying data in S3.

      When you want to filter out specific rows, the common approach is indeed to use CTAS (Create Table As Select). Sure, it might feel like a hassle or make things a little cluttered in your S3 bucket, but it’s one of the main ways people handle data curation in Athena.

      Here’s a simple example of how you might do it:

              CREATE TABLE new_table
        AS SELECT * FROM old_table
        WHERE activity_date >= DATE_ADD('year', -1, CURRENT_DATE())

      This will create a new table new_table with only the rows where the user has been active in the last year. I know it can feel annoying to keep creating new tables, but sometimes it’s just part of the process with Athena!

      Another thing to consider is using views. You can create a view based on your queries, which can make it easier to reference your filtered data without generating new tables. This might help you to avoid clutter!

      Example for creating a view:

              CREATE VIEW active_users AS 
        SELECT * FROM user_activity_logs
        WHERE activity_date >= DATE_ADD('year', -1, CURRENT_DATE())

      This way, whenever you query active_users, it’ll just show the filtered results. No need to create new tables all the time. But, keep in mind that the view still queries the original data every time, so performance can vary depending on the size of your data.

      Hope that helps a bit! Let me know if you have more questions or if there’s something else you’re stuck on!



    2. AWS Athena is a powerful query service that operates on the data stored in S3, but it does have some limitations regarding data manipulation operations like DELETE. In Athena, you cannot directly delete specific rows from a table as you would in traditional SQL databases. Instead, Athena’s architecture relies on a read-only model for the data in S3, which means modifications to the data aren’t supported natively. However, you can achieve similar results by using a technique known as Create Table As Select (CTAS). With CTAS, you can write a query that selects only the rows you wish to keep, effectively filtering your data based on specific criteria. Although this isn’t as straightforward as a DELETE command, it allows for a refined dataset that you can work with going forward.

      To manage the number of tables cluttering your S3 bucket, consider using a systematic naming convention or a temporary storage strategy where you create intermediate tables for your analysis and then drop or overwrite them after you’re done. Another approach you might consider is leveraging AWS Glue in conjunction with Athena, which can help you manage schemas and catalogs effectively. While CTAS may feel cumbersome for frequent filtering, it provides flexibility in how you can work with your data without modifying the original datasets. Depending on your analysis needs, using views (if applicable) or rethinking your data organization strategy might also present more manageable solutions. Overall, while there are workarounds for row exclusion in Athena, it often requires some creativity to avoid proliferation of datasets in S3.


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