Spark: Cluster Computing with Working Sets This paper focuses on applications that reuse a working set of data across multiple parallel operations and proposes a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of Map Reduce.Tags: Sdan Skriver Du Et Godt Engelsk EssayInternational Review Of Business Research PapersEssay On Kite Runner BetrayalEssays About RacismResearch Paper Writing FormatLawyer Research PaperSchooling Vs Education EssaySuper Generic Essay Outline
Chukwa: A large-scale monitoring system This paper describes the design and initial implementation of Chukwa, a data collection system for monitoring and analyzing large distributed systems.
Chukwa is built on top of Hadoop, an open source distributed filesystem and Map Reduce implementation, and inherits Hadoop’s scalability and robustness.
Shark marries query processing with deep data analysis, providing a unified system for easy data manipulation using SQL and pushing sophisticated analysis closer to data.
The Page Rank Citation Ranking: Bringing Order to the Web This paper describes Page Rank, a method for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them.
The Chubby lock service for loosely-coupled distributed systems Chubby is a distributed lock service; it does a lot of the hard parts of building distributed systems and provides its users with a familiar interface (writing files, taking a lock, file permissions).
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The paper describes it, focusing on the API rather than the implementation details.He was the man who first conceived of the relational model for database management.Map-Reduce for Machine Learning on Multicore The paper focuses on developing a general and exact technique for parallel programming of a large class of machine learning algorithms for multicore processors.Finding a needle in Haystack: Facebook’s photo storage This paper describes Haystack, an object storage system optimized for Facebook’s Photos application.Facebook currently stores over 260 billion images, which translates to over 20 petabytes of data.The central idea is to allow a future programmer or user to speed up machine learning applications by “throwing more cores” at the problem rather than search for specialized optimizations.Megastore: Providing Scalable, Highly Available Storage for Interactive Services This paper describes Megastore, a storage system developed to blend the scalability of a No SQL datastore with the convenience of a traditional RDBMS in a novel way.This paper explores the feasibility of building a hybrid system. This paper outlines the S4 architecture in detail, describes various applications, including real-life deployments, to show that the S4 design is surprisingly flexible and lends itself to run in large clusters built with commodity hardware.Dremel: Interactive Analysis of Web-Scale Datasets This paper describes the architecture and implementation of Dremel, a scalable, interactive ad-hoc query system for analysis of read-only nested data, and explains how it complements Map Reduce-based computing.f you are looking for some of the most influential research papers that revolutionised the way how we gather, aggregate, analyze and store increasing volumes of data in a short span of 10 years, you are in the right place!These papers were shortlisted, based on recommendations by big data enthusiasts and experts around the globe from various social media channels.