Create table from custom partitions

I have data on s3 partitioned in custom format, which is not Hive compatible. Is there a way to create a table out of several locations on S3?

examples of locations:

1 Like


You have 2 choices

#1 promote user-repo as a S3 folder and we will treat the 2 sub-folders as partitions
#2 Map to table using AWS Glue

Hi @balaji.ramaswamy,

Thank you for your replay!

Partition format is custom. Can you please provide an example of how that data query will work? How it can filter out partitions in this case?

The queries we have need to filter out data by different sources in some specific time periods. For example,
SELECT user_id, event_id, date_created
FROM users
WHERE source=10 AND date_created BETWEEN 1538123952000 AND 1546331952000 AND event_id=100

If I promote some sources to tables, then I probably will be able to distinguish between sources by adding them into table name, like users_10. As of compaction periods, I’m not sure how it will work.


What column is your partition on ? If the FILTER is on the partition column then will be considered for pruning else it would be a FILTER push down


The data is partitioned by source and date_created column periods, called “compaction_period” (no column with compaction period present in data).

Sorry, I do not understand how it can prune partitions on data that are not partitioned in hive partition format. Unless you have a way to define partition format on a level of additional metadata like iceberg does, I do not see a way for dremio leverage the fact that data is partitioned by src and date_created periods.

From other words, the problem here that hive partition format for such data would look like:
whereas the data we have, follows following layout:

Please notice, the data we have does not have column names in its path neither equality sign.

Do you have a way to programatically promote s3 folder to a table? For now, let’s focus on option #1.


Do you have a way to programmatically promote s3 folder to a table?

Yes on the S3 source you can click on metadata tab and check “Automatically format files into physical datasets when users issue queries.”

So at whatever level you want to format you can fire the query, you can also promote via rest API