Identify time partitions in a Microsoft Azure dataset

Identify the time partitions in your Microsoft Azure datasets to make searches more efficient and cost effective.

Partitioning is an organization strategy for large datasets that makes it possible for you to search them efficiently. When you partition your data, you organize it into a hierarchical directory structure based on the distinct values of one or more fields in the data.

For example, you might partition your application logs in Microsoft Azure by date, breaking them down by year, month, and day. Then you can place files corresponding to a single day's worth of data in a Microsoft Azure path like:

  • Hive-style: https://my_azure_storage_account.blob.core.windows.net/my_container/logs/year=2025/month=08/day=23/
  • Non-Hive: https://my_azure_storage_account.blob.core.windows.net/my_container/logs/2025/08/23/

If you have a partitioned dataset in Microsoft Azure, you can identify the time fields that make up the hierarchical structure of the data partitions. When you filter your federated searches with time partition values, those searches become more efficient and cost effective.

Note: You can identify time partition settings for a dataset even if you have not selected Define the time field. Time partition fields exist in the Amazon S3 paths that form the structure of your dataset. The Time field exists as a column in the data catalog that references your dataset.

When you define time partitions for a dataset, start with the first field by which data is partitioned, then list the second field, and so on. For example, say your data catalog references a dataset that you have partitioned by year, month, and day, like this: https://my_azure_storage_account.blob.core.windows.net/my_container/logs/year=2025/month=08/day=23/ In this case you would identify year as the first time partition field, month as the second time partition field, and day as the third time partition field.

Follow the link that is most appropriate for your needs: