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Atul Sikaria

dblp:199/6357 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 67% Cloud and datacenter computing · 33%
Databases, data mining, and information retrieval
1 paper
Distributed and cloud data management · 77% Database system architecture and tuning · 23%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
datacenter storage
0.512021
Facebook's Tectonic Filesystem: Efficiency from Exascale · FAST 2021
Storage systems › file systems
distributed file system
0.512021
Facebook's Tectonic Filesystem: Efficiency from Exascale · FAST 2021
Storage systems › distributed storage
exascale storage system
0.512021
Facebook's Tectonic Filesystem: Efficiency from Exascale · FAST 2021
Distributed and cloud data management › distributed data store
distributed file system
0.312017
Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics · SIGMOD Conference 2017

Methods — techniques the papers use, named apart from their topics

metadata sharding · 0.5erasure coding · 0.5
YearPublicationVenuePosition
2021 Facebook's Tectonic Filesystem: Efficiency from Exascale
Satadru Pan, Theano Stavrinos, Yunqiao Zhang, Atul Sikaria, Pavel Zakharov, Shiva Shankar P., Mike Shuey, Richard Wareing, Monika Gangapuram, Guanglei Cao, Christian Preseau, Pratap Singh, Kestutis Patiejunas, J. R. Tipton, Ethan Katz-Bassett, Wyatt Lloyd
FAST4
2017 Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics
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.
Raghu Ramakrishnan 0001, Baskar Sridharan, John R. Douceur, Pavan Kasturi, Balaji Krishnamachari-Sampath, Karthick Krishnamoorthy, 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
SIGMOD Conference18