Raul Castro Fernandez, Peter Pietzuch, Jay Kreps, Neha Narkhede, Jun Rao, Joel Koshy, Dong Lin, Chris Riccomini, and Guozhang Wang

In the 7th Biennial Conference on Innovative Data Systems Research



With more sophisticated data-parallel processing systems, the new bottleneck in data-intensive companies shifts from the back-end data systems to the data integration stack, which is responsible for the pre-processing of data for back-end applications. The use of back-end data systems with different access latencies and data integration requirements poses new challenges that current data integration stacks based on distributed file systems—proposed a decade ago for batch-oriented processing—cannot address. In this paper, we describe Liquid, a data integration stack that provides low latency data access to support near real-time in addition to batch applications. It supports incremental processing, and is cost-efficient and highly available. Liquid has two layers: a processing layer based on a stateful stream processing model, and a messaging layer with a highly-available publish/subscribe system. We report our experience of a Liquid deployment with back- end data systems at LinkedIn, a data-intensive company with over 300 million users.