Building a database on S3

By M. Brantner, D. Florescu, David A. Graf, Donald Kossmann, Tim Kraska. SIGMOD Conference, 2008

331 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/d3928410725762070b2fd71e1432a8a0716d4e4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

Citations in the corpus

Big data and cloud computing: current state and future opportunities (2011, neutral): “These systems include ElasTraS [12, 13, 15], DBonS3 [7, 23], Project Deuteronomy [24], Relational Cloud [11], epiC [29], Hyder [6] to name a few.”
Spanner: Google's Globally-Distributed Database (2012, neutral): “They point to several other efforts that have been made to explore database functionality layered on distributed key-value stores [1, 4, 7, 41] as evidence that the two worlds are converging.”
Data Management in the Cloud: Limitations and Opportunities (2009, result): “Similarly, the research done by Brantner et. al. found that they needed to relax consistency and isolation guarantees in the database they built on top of Amazon’s S3 storage layer [12].”
Methodological Review: Cloud computing: A new business paradigm for biomedical information sharing (2010, background): “On the other hand, the robust, distributed S3 storage poses problems for DBMS capabilities [32].”
An evaluation of alternative architectures for transaction processing in the cloud (2010, methodology): “Higher levels of consistency can be implemented using the other protocols of [3] but were not considered as part of our study in order not to lose focus.”
The Case for Learned Index Structures (2017, background): “This is similar to how indexes are implemented in key value stores [12, 21] or how B-Trees on modern hardware are designed [44].”
Relational Cloud: a Database Service for the cloud (2011, methodology): “Approaches include NoSQL systems [3, 4, 2, 14], which sacrifice a fair amount of expressive power and/or consistency in favor of extreme scalability, and SQL-based systems that limit the type of transactions allowed [11, 7].”
Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics (2021, methodology): “Regardless of the exact design, however, the core challenge is that the data storage format becomes part of the system’s public API to allow fast direct access, unlike in a traditional DBMS.”
A Survey of Large Scale Data Management Approaches in Cloud Environments (2011, background): “[39] have presented initial efforts of building Web-based database applications on top of S3.”