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

Summary: ADLS is a fully-managed, exabyte-scale file service optimized for parallel big-data analytics, unifying HDFS compatibility with Cosmos semantics and co-located compute/data. It bridges Cosmos and Hadoop with an HDFS-compatible API, adds multi-tier storage and security, and outlines Cosmos-to-ADLS migration. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5410
Venue
SIGMOD
Year
2017
Pagerank
8.5239378e-05
Overall Rank
2,477 | 83.01%
DOI
10.1145/3035918.3056100

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{ramakrishnan_sigmod17,
        title = {{Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics}},
        author = {Ramakrishnan, Raghu and Sridharan, Baskar and Douceur, John R. and Kasturi, Pavan and Krishnamachari-Sampath, Balaji and Krishnamoorthy, Karthick and Li, Peng and Manu, Mitica and Michaylov, Spiro and Ramos, Rogério and Sharman, Neil and Xu, Zee and Barakat, Youssef and Douglas, Chris and Draves, Richard and Naidu, Shrikant S and Shastry, Shankar and Sikaria, Atul and Sun, Simon and Venkatesan, Ramarathnam},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3056100},
        url = {https://dl.acm.org/doi/10.1145/3035918.3056100},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 23 of 23 citing papers.

Rank Citing Paper Year Venue Pagerank
1,453 Dremel: A Decade of Interactive SQL Analysis at Web Scale 2020 VLDB 0.00010742227
1,468 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010686496
1,765 Selecting Subexpressions to Materialize at Datacenter Scale 2018 VLDB 9.8079546e-05
1,925 POLARIS: The Distributed SQL Engine in Azure Synapse 2020 VLDB 9.4764398e-05
2,025 Data Market Platforms: Trading Data Assets to Solve Data Problems 2020 VLDB 9.2903125e-05
2,651 Magpie: Python at Speed and Scale using Cloud Backends 2021 CIDR 8.2918086e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
2,865 End-to-end Optimization of Machine Learning Prediction Queries 2022 SIGMOD 8.0180243e-05
5,037 A Deep Dive into Common Open Formats for Analytical DBMSs 2023 VLDB 6.3914026e-05
5,101 To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams 2021 SIGMOD 6.3642265e-05
6,045 Helios: Hyperscale Indexing for the Cloud & Edge 2020 VLDB 5.9957544e-05
6,121 The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward 2021 VLDB 5.9688569e-05
6,194 Incorporating Super-Operators in Big-Data Query Optimizers 2020 VLDB 5.9470844e-05
6,946 KEA: Tuning an Exabyte-Scale Data Infrastructure 2021 SIGMOD 5.7309848e-05
7,762 Runtime Variation in Big Data Analytics 2023 SIGMOD 5.5501898e-05
8,708 Extending Polaris to Support Transactions 2024 SIGMOD 5.3794896e-05
8,927 Hyperspace: The Indexing Subsystem of Azure Synapse 2021 VLDB 5.3483178e-05
8,947 Bringing Cloud-Native Storage to SAP IQ 2021 SIGMOD 5.3475158e-05
9,224 Phoebe: A Learning-based Checkpoint Optimizer 2021 VLDB 5.3035811e-05
9,378 AutoComp: Automated Data Compaction for Log-Structured Tables in Data Lakes 2025 SIGMOD 5.2755515e-05
9,836 Towards Functional Decomposition of Storage Formats 2025 CIDR 5.2110542e-05
11,030 GraphAr: An Efficient Storage Scheme for Graph Data in Data Lakes 2025 VLDB 5.093636e-05
11,418 Efficient Approximation Framework for Attribute Recommendation 2023 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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