PPT-Bigtable : A Distributed Storage System for Structured Data

Fay Chang et al Google Inc Presenter Kyungho Jeon kyunghojbuffaloedu 10222012 Fall 2012 CSE 704 Webscale Data Management 1 Motivation and Design Goal Distributed Storage System for Structured Data. S. Sudarshan. IIT Bombay. Derived from an earlier talk by S. . Sudarshan, presented . at the. MSRI Summer School on Distributed Systems, May/June 2012. Why Distributed Data Storage a.k.a. Cloud Storage. : . A Fault-Tolerant Abstraction for In-Memory Cluster Computing. Matei. . Zaharia. , . Mosharaf. Chowdhury, . Tathagata. Das, . Ankur. Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott . Abstract. Remote data integrity checking is of crucial . impor¬tance. in cloud storage. It can make the clients verify whether their outsourced data is kept intact without downloading the whole data. In some application scenarios, the clients have to store their data on multi-cloud servers. At the same time, the integrity checking protocol must be efficient in order to save the verifier’s cost.. Presented by: Ayesha Fawad. . 10/07/2014. 1. Overview. Google File System. . Basics. Design. . Chunks. R. eplicas. . Clusters. . Client. 2. Overview. Google . File . System. . in Big Data . and Analytics. Introduction to . Hadoop. Eco-System. Agenda. In this session, you will learn about:. . What is Hadoop. ?. Why Hadoop?. Advantages of Hadoop?. History of . Hadoop. Key Characteristics of . Group ID: 4. Baolin Wu, . Liushan. Yang, . Pengyu. . Ji. Motivation & Goals. Distributed File System provides more reliable service.. Cloud Storage Service is very popular. Google Cloud Storage, Dropbox, Amazon SC3 have many users.. Management System . Presented by. Jayesh Kawli. Introduction. D. istributed . database system . with . combination . of technologies from . Amazon Dynamo and Google . BigTable . Abstract. Distributed Storage System. Petabytes of Structured Data. Web indexing, Google Earth, Google Finance……. 1. Introduction. Goals: wide applicability, scalability, high performance, high availability. : . A Fault-Tolerant Abstraction for In-Memory Cluster Computing. Matei. . Zaharia. , . Mosharaf. Chowdhury, . Tathagata. Das, . Ankur. Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott . 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 . Introduction. Parallel machines have become quite common and affordable. prices of microprocessors, memory and disks have dropped sharply. Data storage needs are growing increasingly large. user data at web-scale. 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. Papadakis Harris. Dept. of Engineering Informatics. ΤΕΙ . of Crete. Client/Server Paradigm. Basic idea: to structure the system as a set of collaborating processes. Servers (or servers) that offer their services. 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.