Azure Data Lake Storage Gen2 for Analytics — Choosing the Primary Storage Layer

Quick Answer

ADLS Gen2 is the right fit here because it is built specifically to serve as a shared, general-purpose analytics store that multiple types of consumers can hit at once without stepping on each other. Underneath, it is still Azure Blob Storage, so it inherits massive scalability and low-cost object storage, but the added hierarchical namespace organizes that storage into real directories, which both improves performance for engines reading large numbers of Parquet files and enables POSIX-style access control lists at the folder level. That combination matters directly for this scenario: data scientists running batch training jobs typically need high-throughput, parallel reads across large volumes of files, the kind of access pattern Spark or similar engines rely on, while analysts running ad-hoc queries need to be able to reach specific datasets without being granted access to everything in the account. A flat, non-hierarchical store would struggle to offer that same directory-level security boundary. Recognize this pattern in future questions: whenever a scenario describes a single storage layer that needs to serve both large-scale batch analytics workloads and more selective, permission-sensitive ad-hoc access, especially with structured file formats like Parquet, ADLS Gen2 is the service designed to satisfy both needs simultaneously, rather than requiring separate storage systems for each audience.