Liquid: Unifying Nearline and Offline Big Data Integration
Year: 2015
Type: conference-paper
Abstract: 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 backend data systems at LinkedIn, a data-intensive company with over 300 million users.
Source: Spiral (Imperial College London)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2294178609