Azure's hyperscale storage service purpose-built for big data analytics workloads. Built on top of Azure Blob Storage, it adds a hierarchical namespace — organizing files into real directories the way a traditional filesystem does — and exposes a Hadoop-compatible interface (ABFS) so big data tools like Apache Spark and Hive can read and write data using the same APIs they use on-premises. ADLS Gen2 is the recommended storage foundation for both Azure Synapse Analytics and Databricks on Azure: data lands here first, pipelines transform it in place, and analytics engines query it directly without copying data to a separate store. Fine-grained access control via POSIX-style ACLs lets security teams apply file- and folder-path-level permissions without moving data.
A retail company stores petabytes of raw clickstream logs in ADLS Gen2 organized under a hierarchical path like /raw/year/month/day/. Azure Synapse serverless SQL queries the raw layer directly for ad-hoc reports, while a Databricks Spark job reads the same files to build Delta Lake tables in the /curated/ layer — all without duplicating any data.
These services provide scalable storage solutions for big data analytics, each with unique features like hierarchical namespaces or integration with analytics tools.