Bhaskar Ghosh, Shirshanka Das, Jay Kreps, Kapil Surlaker, Jun Rao, et. al.

In 28th IEEE International Conference on Data Engineering (ICDE 2012)


 

Abstract

LinkedIn is among the largest social networking sites in the world. As the company has grown, our core data sets and request processing requirements have grown as well. In this paper, we describe a few selected data infrastructure projects at Linked In that have helped us accommodate this increasing scale. Most of those projects build on existing open source projects and are themselves available as open source. The projects covered in this paper include: (1) Voldemort: a scalable and fault tolerant key-value store, (2) Data bus: a framework for delivering database changes to downstream applications, (3) Espresso: a distributed data store that supports flexible schemas and secondary indexing, (4) Kafka: a scalable and efficient messaging system for collecting various user activity events and log data.

BiBTeX

@inproceedings{auradkar2012data, title={Data Infrastructure at LinkedIn}, author={Auradkar, A. and Botev, C. and Das, S. and De Maagd, D. and Feinberg, A. and Ganti, P. and Gao, L. and Ghosh, B. and Gopalakrishna, K. and Harris, B. and others}, booktitle={2012 IEEE 28th International Conference on Data Engineering}, pages={1370--1381}, year={2012}, organization={IEEE} }