Hi @Anshal
Thanks for using Microsoft Q&A forum and posting your query.
In general, the naming convention you mentioned is a way to organize data in Azure Data Lake Storage (ADLS) zones. It uses a directory structure to group data based on different attributes, such as date, time or location. The advantages of this naming conventions are to allow better data organization, filtered searches, security, and automation in the processing. The level of granularity for the date structure is determined by the interval on which the data is uploaded or processed, such as hourly, daily, or even monthly. It helps to improve query performance, makes it easier to manage large volumes of data, and provides flexibility in how data is organized and accessed.
Below are few highlights or benefits of partitioning data in ADLS zones as per the mentioned naming convention:
Improved query performance: Partitioning data based on relevant attributes can significantly improve query performance by reducing the amount of data that needs to be scanned.
Easier data management: Partitioning data into subdirectories based on relevant attributes can make it easier to manage and organize large volumes of data.
Scalability: Partitioning data can help improve scalability by allowing data to be distributed across multiple nodes or clusters.
- Flexibility: Partitioning data based on different criteria can provide flexibility in how data is organized and accessed, making it easier to adapt to changing business needs.
In addition, I would recommend going through below blogs for additional information.
- FAQs About Organizing a Data Lake
- Data Lake Use Cases and Planning Considerations
- Best practices for using Azure Data Lake Storage Gen2 - Directory structure - This document has few examples with uses case of this naming conventions.
Hope this info helps.
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