PAPER DIGEST
Most Influential SIGMOD 2017 Paper · 2026-03 edition

Azure Data Lake Store: A Hyperscale Distributed File Service For Big Data Analytics

Raghu Ramakrishnan, Baskar Sridharan, John R. Douceur, Pavan Kasturi, Balaji Krishnamachari-Sampath, Karthick Krishnamoorthy, Peng Li, Mitica Manu, Spiro Michaylov, Rogério Ramos, Neil Sharman, Zee Xu, Youssef Barakat, Chris Douglas, Richard Draves, Shrikant S. Naidu, Shankar Shastry, Atul Sikaria, Simon Sun, Ramarathnam Venkatesan

Venue
ACM SIGMOD Conference (SIGMOD) 2017
Recognition
Most Influential SIGMOD 2017 Paper (Rank No. 15)
Edition
2026-03
Impact factor
5
Certificate ID
6e2335ac8ffafef0

Abstract

Azure Data Lake Store (ADLS) is a fully-managed, elastic, scalable, and secure file system that supports Hadoop distributed file system (HDFS) and Cosmos semantics. It is specifically designed and optimized for a broad spectrum of Big Data analytics that depend on a very high degree of parallel reads and writes, as well as collocation of compute and data for high bandwidth and low-latency access. It brings together key components and features of Microsoft?s Cosmos file system-long used by internal customers at Microsoft and HDFS, and is a unified file storage solution for analytics on Azure. Internal and external workloads run on this unified platform. Distinguishing aspects of ADLS include its design for handling multiple storage tiers, exabyte scale, and comprehensive security and data sharing features. We present an overview of ADLS architecture, design points, and performance.

Download PDF certificate