Big Data Analytics Options on AWS
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Reliability and Security of Large Scale Data Storage in Cloud Computing C.W
This article is part of the Reliability Society 2010 Annual Technical Report Reliability and Security of Large Scale Data Storage in Cloud Computing C.W. Hsu ¹¹¹ C.W. Wang ²²² Shiuhpyng Shieh ³³³ Department of Computer Science Taiwan Information Security Center at National Chiao Tung University NCTU (TWISC@NCTU) Email: ¹[email protected] ³[email protected] ²[email protected] Abstract In this article, we will present new challenges to the large scale cloud computing, namely reliability and security. Recent progress in the research area will be briefly reviewed. It has been proved that it is impossible to satisfy consistency, availability, and partitioned-tolerance simultaneously. Tradeoff becomes crucial among these factors to choose a suitable data access model for a distributed storage system. In this article, new security issues will be addressed and potential solutions will be discussed. Introduction “Cloud Computing ”, a novel computing model for large-scale application, has attracted much attention from both academic and industry. Since 2006, Google Inc. Chief Executive Eric Schmidt introduced the term into industry, and related research had been carried out in recent years. Cloud computing provides scalable deployment and fine-grained management of services through efficient resource sharing mechanism (most of them are for hardware resources, such as VM). Convinced by the manner called “ pay-as-you-go ” [1][2], companies can lower the cost of equipment management including machine purchasing, human maintenance, data backup, etc. Many cloud services rapidly appear on the Internet, such as Amazon’s EC2, Microsoft’s Azure, Google’s Gmail, Facebook, and so on. However, the well-known definition of the term “Cloud Computing ” was first given by Prof. -
Timeline 1994 July Company Incorporated 1995 July Amazon
Timeline 1994 July Company Incorporated 1995 July Amazon.com Sells First Book, “Fluid Concepts & Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought” 1996 July Launches Amazon.com Associates Program 1997 May Announces IPO, Begins Trading on NASDAQ Under “AMZN” September Introduces 1-ClickTM Shopping November Opens Fulfillment Center in New Castle, Delaware 1998 February Launches Amazon.com Advantage Program April Acquires Internet Movie Database June Opens Music Store October Launches First International Sites, Amazon.co.uk (UK) and Amazon.de (Germany) November Opens DVD/Video Store 1999 January Opens Fulfillment Center in Fernley, Nevada March Launches Amazon.com Auctions April Opens Fulfillment Center in Coffeyville, Kansas May Opens Fulfillment Centers in Campbellsville and Lexington, Kentucky June Acquires Alexa Internet July Opens Consumer Electronics, and Toys & Games Stores September Launches zShops October Opens Customer Service Center in Tacoma, Washington Acquires Tool Crib of the North’s Online and Catalog Sales Division November Opens Home Improvement, Software, Video Games and Gift Ideas Stores December Jeff Bezos Named TIME Magazine “Person Of The Year” 2000 January Opens Customer Service Center in Huntington, West Virginia May Opens Kitchen Store August Announces Toys “R” Us Alliance Launches Amazon.fr (France) October Opens Camera & Photo Store November Launches Amazon.co.jp (Japan) Launches Marketplace Introduces First Free Super Saver Shipping Offer (Orders Over $100) 2001 April Announces Borders Group Alliance August Introduces In-Store Pick Up September Announces Target Stores Alliance October Introduces Look Inside The BookTM 2002 June Launches Amazon.ca (Canada) July Launches Amazon Web Services August Lowers Free Super Saver Shipping Threshold to $25 September Opens Office Products Store November Opens Apparel & Accessories Store 2003 April Announces National Basketball Association Alliance June Launches Amazon Services, Inc. -
Data Centers and Cloud Computing Data Centers
Data Centers and Cloud Computing Data Centers • Large server and storage farms – 1000s of servers • Intro. to Data centers – Many TBs or PBs of data • Virtualization Basics • Used by – Enterprises for server applications – Internet companies • Intro. to Cloud Computing • Some of the biggest DCs are owned by Google, Facebook, etc • Used for – Data processing – Web sites – Business apps Computer Science Lecture 22, page 2 Computer Science Lecture 22, page 3 Inside a Data Center MGHPCC Data Center • Giant warehouse filled with: • Racks of servers • Storage arrays • Cooling infrastructure • Power converters • Backup generators • Data center in Holyoke Computer Science Lecture 22, page 4 Computer Science Lecture 22, page 5 Modular Data Center Virtualization • ...or use shipping containers • Each container filled with thousands of servers • Can easily add new containers • Virtualization: extend or replace an existing interface to – “Plug and play” mimic the behavior of another system. – Just add electricity – Introduced in 1970s: run legacy software on newer mainframe hardware • Allows data center to be easily • Handle platform diversity by running apps in VMs expanded – Portability and flexibility • Pre-assembled, cheaper Computer Science Lecture 22, page 6 Computer Science Lecture 22, page 7 Types of Interfaces Types of OS-level Virtualization • Different types of interfaces – Assembly instructions – System calls • Type 1: hypervisor runs on “bare metal” – APIs • Type 2: hypervisor runs on a host OS • Depending on what is replaced /mimiced, -
How Big Data Enables Economic Harm to Consumers, Especially to Low-Income and Other Vulnerable Sectors of the Population
How Big Data Enables Economic Harm to Consumers, Especially to Low-Income and Other Vulnerable Sectors of the Population The author of these comments, Nathan Newman, has been writing for twenty years about the impact of technology on society, including his 2002 book Net Loss: Internet Profits, Private Profits and the Costs to Community, based on doctoral research on rising regional economic inequality in Silicon Valley and the nation and the role of Internet policy in shaping economic opportunity. He has been writing extensively about big data as a research fellow at the New York University Information Law Institute for the last two years, including authoring two law reviews this year on the subject. These comments are adapted with some additional research from one of those law reviews, "The Costs of Lost Privacy: Consumer Harm and Rising Economic Inequality in the Age of Google” published in the William Mitchell Law Review. He has a J.D. from Yale Law School and a Ph.D. from UC-Berkeley’s Sociology Department; Executive Summary: ϋBͤ͢ Dͯ͜͜ό ͫͧͯͪͭͨͮ͜͡ ͮͰͣ͞ ͮ͜ Gͪͪͧ͢͠ ͩ͜͟ Fͪͪͦ͜͞͠͝ ͭ͜͠ ͪͨͤͩ͢͝͠͞ ͪͨͤͩͩͯ͟͜ institutions organizing information not just about the world but about consumers themselves, thereby reshaping a range of markets based on empowering a narrow set of corporate advertisers and others to prey on consumers based on behavioral profiling. While big data can benefit consumers in certain instances, there are a range of new consumer harms to users from its unregulated use by increasingly centralized data platforms. A. These harms start with the individual surveillance of users by employers, financial institutions, the government and other players that these platforms allow, but also extend to ͪͭͨͮ͡ ͪ͡ Ͳͣͯ͜ ͣͮ͜ ͩ͝͠͠ ͧͧ͜͟͞͠ ϋͧͪͭͤͯͣͨͤ͜͢͞ ͫͭͪͤͧͤͩ͢͡ό ͯͣͯ͜ ͧͧͪ͜Ͳ ͪͭͫͪͭͯ͜͞͠ ͤͩͮͯͤͯͰͯͤͪͩͮ to discriminate and exploit consumers as categorical groups. -
Amazon Dynamodb
Dynamo Amazon DynamoDB Nicolas Travers Inspiré de Advait Deo Vertigo N. Travers ESILV : Dynamo Amazon DynamoDB – What is it ? • Fully managed nosql database service on AWS • Data model in the form of tables • Data stored in the form of items (name – value attributes) • Automatic scaling ▫ Provisioned throughput ▫ Storage scaling ▫ Distributed architecture • Easy Administration • Monitoring of tables using CloudWatch • Integration with EMR (Elastic MapReduce) ▫ Analyze data and store in S3 Vertigo N. Travers ESILV : Dynamo Amazon DynamoDB – What is it ? key=value key=value key=value key=value Table Item (64KB max) Attributes • Primary key (mandatory for every table) ▫ Hash or Hash + Range • Data model in the form of tables • Data stored in the form of items (name – value attributes) • Secondary Indexes for improved performance ▫ Local secondary index ▫ Global secondary index • Scalar data type (number, string etc) or multi-valued data type (sets) Vertigo N. Travers ESILV : Dynamo DynamoDB Architecture • True distributed architecture • Data is spread across hundreds of servers called storage nodes • Hundreds of servers form a cluster in the form of a “ring” • Client application can connect using one of the two approaches ▫ Routing using a load balancer ▫ Client-library that reflects Dynamo’s partitioning scheme and can determine the storage host to connect • Advantage of load balancer – no need for dynamo specific code in client application • Advantage of client-library – saves 1 network hop to load balancer • Synchronous replication is not achievable for high availability and scalability requirement at amazon • DynamoDB is designed to be “always writable” storage solution • Allows multiple versions of data on multiple storage nodes • Conflict resolution happens while reads and NOT during writes ▫ Syntactic conflict resolution ▫ Symantec conflict resolution Vertigo N. -
Deploy, Operate, and Evolve Your Data Center: HP Datacenter Care
Brochure Deploy, operate, and evolve your data center HP Datacenter Care The data center is evolving—shouldn’t Improve agility, scalability—react at the your support keep pace? speed of business Historically, business-critical IT has been delivered on dedicated, HP Datacenter Care service is designed to support this homogenous, and proprietary infrastructures. In this siloed results-oriented approach by providing an environment-wide IT model, performance, uptime, and security outweighed support solution tailored to your needs. HP Datacenter Care considerations of speed, agility, and cost. Now, however, the trends is a flexible, comprehensive, relationship-based approach to of mobility, big data, and cloud computing are fundamentally personalized support and management of heterogeneous data changing how you deliver information, and how technology is centers. Datacenter Care is a structured framework of repeatable, implemented, moving closer to unconstrained access to IT. tested, and globally available services “building blocks.” You have an account support manager (ASM) who knows your business and your IT is delivered as services anywhere, across hybrid deployments of IT environment, and who can help you select the services you need private, managed, and public cloud, as well as traditional IT. It’s what from an extensive portfolio of support and consulting services. The HP is developing as the Converged Cloud, and it can finally allow ASM leverages our experience in supporting complex environments, enterprises to achieve business agility, with choice and improved global support partnerships, and technical expertise. ROI. Today, HP is a leader in the industry for support services, for customers worldwide who need to keep their systems running, So you get exactly the services you need—when and where you reduce costs, and avoid issues. -
Issues, Challenges, and Solutions: Big Data Mining
ISSUES , CHALLENGES , AND SOLUTIONS : BIG DATA MINING Jaseena K.U. 1 and Julie M. David 2 1,2 Department of Computer Applications, M.E.S College, Marampally, Aluva, Cochin, India [email protected],[email protected] ABSTRACT Data has become an indispensable part of every economy, industry, organization, business function and individual. Big Data is a term used to identify the datasets that whose size is beyond the ability of typical database software tools to store, manage and analyze. The Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper presents the literature review about the Big data Mining and the issues and challenges with emphasis on the distinguished features of Big Data. It also discusses some methods to deal with big data. KEYWORDS Big data mining, Security, Hadoop, MapReduce 1. INTRODUCTION Data is the collection of values and variables related in some sense and differing in some other sense. In recent years the sizes of databases have increased rapidly. This has lead to a growing interest in the development of tools capable in the automatic extraction of knowledge from data [1]. Data are collected and analyzed to create information suitable for making decisions. Hence data provide a rich resource for knowledge discovery and decision support. A database is an organized collection of data so that it can easily be accessed, managed, and updated. Data mining is the process discovering interesting knowledge such as associations, patterns, changes, anomalies and significant structures from large amounts of data stored in databases, data warehouses or other information repositories. -
The Cloud-Enabled Data Center
A Revolutionary Technology Extending the Ultra-Broad Functionality of Ethernet White Paper May 2013 The Cloud-Enabled Data Center The Cloud-Enabled Data Center Introduction A growing number of organizations are adopting some form of cloud computing to meet the challenges of rapidly deploying their IT services and addressing their dynamic workload environments while maximizing their IT return on investments (ROIs). Cloud computing gives users the ability to access the IT resources faster than traditional and virtual servers. It also provides improved manageability and requires less maintenance. By deploying technology as a service, your users access only the resources they need for a specific task, which prevents you from incurring costs for computing resources not in use. In addition, developing a cloud-enabled data center can improve operational efficiencies while reducing operational expenses by 50%. Panduit, the leader in Unified Physical Infrastructure offerings, and IBM, a pioneer in delivering cloud computing solutions to clients, have joined forces to deliver optimized, custom and preconfigured solutions for the cloud- enabled data center. This white paper describes how network, storage, compute and operations are important factors to consider when deploying private and public cloud computing in the data center. Specifically, it examines integrated stack/pre- configured and custom cloud deployments. It also explains the importance of the physical infrastructure as the foundation in successful cloud deployment and maintenance. Finally, it showcases how IBM’s depth of experience in delivering systems and software for cloud solutions when combined with Panduit’s physical infrastructure expertise, provides a tremendous impact on room-level data center environmental and new-age topology. -
Performance at Scale with Amazon Elasticache
Performance at Scale with Amazon ElastiCache July 2019 Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved. Contents Introduction .......................................................................................................................... 1 ElastiCache Overview ......................................................................................................... 2 Alternatives to ElastiCache ................................................................................................. 2 Memcached vs. Redis ......................................................................................................... 3 ElastiCache for Memcached ............................................................................................... 5 Architecture with ElastiCache for Memcached ............................................................... -
Oracle Big Data SQL Release 4.1
ORACLE DATA SHEET Oracle Big Data SQL Release 4.1 The unprecedented explosion in data that can be made useful to enterprises – from the Internet of Things, to the social streams of global customer bases – has created a tremendous opportunity for businesses. However, with the enormous possibilities of Big Data, there can also be enormous complexity. Integrating Big Data systems to leverage these vast new data resources with existing information estates can be challenging. Valuable data may be stored in a system separate from where the majority of business-critical operations take place. Moreover, accessing this data may require significant investment in re-developing code for analysis and reporting - delaying access to data as well as reducing the ultimate value of the data to the business. Oracle Big Data SQL enables organizations to immediately analyze data across Apache Hadoop, Apache Kafka, NoSQL, object stores and Oracle Database leveraging their existing SQL skills, security policies and applications with extreme performance. From simplifying data science efforts to unlocking data lakes, Big Data SQL makes the benefits of Big Data available to the largest group of end users possible. KEY FEATURES Rich SQL Processing on All Data • Seamlessly query data across Oracle Oracle Big Data SQL is a data virtualization innovation from Oracle. It is a new Database, Hadoop, object stores, architecture and solution for SQL and other data APIs (such as REST and Node.js) on Kafka and NoSQL sources disparate data sets, seamlessly integrating data in Apache Hadoop, Apache Kafka, • Runs all Oracle SQL queries without modification – preserving application object stores and a number of NoSQL databases with data stored in Oracle Database. -
Amazon Documentdb Deep Dive
DAT326 Amazon DocumentDB deep dive Joseph Idziorek Antra Grover Principal Product Manager Software Development Engineer Amazon Web Services Fulfillment By Amazon © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda What is the purpose of a document database? What customer problems does Amazon DocumentDB (with MongoDB compatibility) solve and how? Customer use case and learnings: Fulfillment by Amazon What did we deliver for customers this year? What’s next? © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Purpose-built databases Relational Key value Document In-memory Graph Search Time series Ledger Why document databases? Denormalized data Normalized data model model { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? GET https://api.yelp.com/v3/businesses/{id} { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? response = yelp_api.search_query(term='ice cream', location='austin, tx', sort_by='rating', limit=5) Why document databases? for i in response['businesses']: col.insert_one(i) db.businesses.aggregate([ { $group: { _id: "$price", ratingAvg: { $avg: "$rating"}} } ]) db.businesses.find({ -
Big Data Stream Analysis: a Systematic Literature Review
Kolajo et al. J Big Data (2019) 6:47 https://doi.org/10.1186/s40537-019-0210-7 SURVEY PAPER Open Access Big data stream analysis: a systematic literature review Taiwo Kolajo1,2* , Olawande Daramola3 and Ayodele Adebiyi1,4 *Correspondence: [email protected]; Abstract [email protected] Recently, big data streams have become ubiquitous due to the fact that a number of 1 Department of Computer and Information Sciences, applications generate a huge amount of data at a great velocity. This made it difcult Covenant University, Ota, for existing data mining tools, technologies, methods, and techniques to be applied Nigeria directly on big data streams due to the inherent dynamic characteristics of big data. In Full list of author information is available at the end of the this paper, a systematic review of big data streams analysis which employed a rigorous article and methodical approach to look at the trends of big data stream tools and technolo- gies as well as methods and techniques employed in analysing big data streams. It provides a global view of big data stream tools and technologies and its comparisons. Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. Out of the initial 2295 papers that resulted from the frst search string, 47 papers were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. The study found that scalability, privacy and load balancing issues as well as empirical analysis of big data streams and technologies are still open for further research eforts.