PPT-BigTable

Distributed storage for structured data 1 Dennis Kafura CS5204 Operating Systems Overview Goals scalability petabytes of data thousands of machines applicability to Google applications. 6.814 / 6.830 F2014. Key Concepts: Transactions. . ACID. . Serializability. . Serial equivalence (testing via conflict graph). . Conflict . vs. view . serializability. . Two-phase locking (strict/rigorous). Inexpensive Scalable Information Access. Many Internet applications need to access data for millions of concurrent users. Relational DBMS technology cannot scale to these workloads using commodity hardware. for Internet-Scale Applications. Atul Adya, Gregory Cooper, . Daniel Myers, Michael Piatek. Google Seattle. 1. A Case for Notifications. Problem: . Ensuring cached data is fresh across users and devices. : A Distributed Storage System for Structured Data. F. Chang, J. Dean, S. . Ghemawat. , W.C. Hsieh, D.A. Wallach. M. Burrows, T. Chandra, A. . Fikes. , R.E. . Gruber. Google, Inc. .. Tianyang. . HU. for Internet-Scale Applications. Atul Adya, Gregory Cooper, . Daniel Myers, Michael Piatek. Google Seattle. 1. A Case for Notifications. Problem: . Ensuring cached data is fresh across users and devices. Kit . How to estimate and measure “the Cloud”. COCOMO 2012. October 2012. David Seaver. OPS Consulting. davidseaver@opsconsulting.com. Some Perspective. The DoD Cloud Computing Strategy introduces an approach to move the Department from the current state of a duplicative, cumbersome, and costly set of application silos to an end state which is an agile, secure, and cost effective service environment that can rapidly respond to changing mission needs.. Presented by: Ayesha Fawad. . 10/07/2014. 1. Overview. Google File System. . Basics. Design. . Chunks. R. eplicas. . Clusters. . Client. 2. Overview. Google . File . System. , Hive, and Pig. Data-Intensive Information Processing Applications ― Session #12. Jimmy Lin. University of Maryland. Tuesday, April 27, 2010. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States. Abstract. Distributed Storage System. Petabytes of Structured Data. Web indexing, Google Earth, Google Finance……. 1. Introduction. Goals: wide applicability, scalability, high performance, high availability. Fay Chang et al. (Google, Inc.). Presenter: . Kyungho. . Jeon. kyunghoj@buffalo.edu. 10/22/2012. Fall 2012: CSE 704 Web-scale Data Management. 1. Motivation and Design Goal. Distributed Storage System for Structured Data. Presenter: Jeffrey Kendall. jdk34@vt.edu. 1. Development began in 2004 at . Google (published 2006). A need to store/handle large amounts of (semi)-structured data. BigTable. Introduction. Many Google projects store data in . Masivo. de . Datos. Otoño. . 2018. Lecture . 8. NoSQL: Overview. Aidan Hogan. aidhog@gmail.com. Hadoop/MapReduce/Pig/Spark:. Processing Un/Structured Information. Information Retrieval:. Storing Unstructured Information. Ashvin Goel. Electrical and Computer Engineering. University of Toronto. ECE 1724, Winter 2021. Web-Scale Apps. Applications that are . hosted in massive-scale . computing infrastructures . such as data centers. Lecture 23. BigTable. /Pond. April 18. th. , 2016. John . Kubiatowicz. . Electrical Engineering and Computer Sciences. University of California, Berkeley. http://www.eecs.berkeley.edu/~kubitron/cs262.