Big Data Tools-An Overview International Journal of Computer
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JETIR Research Journal
© 2018 JETIR October 2018, Volume 5, Issue 10 www.jetir.org (ISSN-2349-5162) QUALITATIVE COMPARISON OF KEY-VALUE BIG DATA DATABASES 1Ahmad Zia Atal, 2Anita Ganpati 1M.Tech Student, 2Professor, 1Department of computer Science, 1Himachal Pradesh University, Shimla, India Abstract: Companies are progressively looking to big data to convey valuable business insights that cannot be taken care by the traditional Relational Database Management System (RDBMS). As a result, a variety of big data databases options have developed. From past 30 years traditional Relational Database Management System (RDBMS) were being used in companies but now they are replaced by the big data. All big bata technologies are intended to conquer the limitations of RDBMS by enabling organizations to extract value from their data. In this paper, three key-value databases are discussed and compared on the basis of some general databases features and system performance features. Keywords: Big data, NoSQL, RDBMS, Riak, Redis, Hibari. I. INTRODUCTION Systems that are designed to store big data are often called NoSQL databases since they do not necessarily depend on the SQL query language used by RDBMS. NoSQL today is the term used to address the class of databases that do not follow Relational Database Management System (RDBMS) principles and are specifically designed to handle the speed and scale of the likes of Google, Facebook, Yahoo, Twitter and many more [1]. Many types of NoSQL database are designed for different use cases. The major categories of NoSQL databases consist of Key-Values store, Column family stores, Document databaseand graph database. Each of these technologies has their own benefits individually but generally Big data use cases are benefited by these technologies. -
LIST of NOSQL DATABASES [Currently 150]
Your Ultimate Guide to the Non - Relational Universe! [the best selected nosql link Archive in the web] ...never miss a conceptual article again... News Feed covering all changes here! NoSQL DEFINITION: Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above. [based on 7 sources, 14 constructive feedback emails (thanks!) and 1 disliking comment . Agree / Disagree? Tell me so! By the way: this is a strong definition and it is out there here since 2009!] LIST OF NOSQL DATABASES [currently 150] Core NoSQL Systems: [Mostly originated out of a Web 2.0 need] Wide Column Store / Column Families Hadoop / HBase API: Java / any writer, Protocol: any write call, Query Method: MapReduce Java / any exec, Replication: HDFS Replication, Written in: Java, Concurrency: ?, Misc: Links: 3 Books [1, 2, 3] Cassandra massively scalable, partitioned row store, masterless architecture, linear scale performance, no single points of failure, read/write support across multiple data centers & cloud availability zones. API / Query Method: CQL and Thrift, replication: peer-to-peer, written in: Java, Concurrency: tunable consistency, Misc: built-in data compression, MapReduce support, primary/secondary indexes, security features. -
The Network Data Storage Model Based on the HTML
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) The network data storage model based on the HTML Lei Li1, Jianping Zhou2 Runhua Wang3, Yi Tang4 1Network Information Center 3Teaching Quality and Assessment Office Chongqing University of Science and Technology Chongqing University of Science and Technology Chongqing, China Chongqing, China [email protected] [email protected] 2Network Information Center 4School of Electronic and Information Engineering Chongqing University of Science and Technology Chongqing University of Science and Technology Chongqing, China Chongqing, China [email protected] [email protected] Abstract—in this paper, the mass oriented HTML data refers popular NoSQL for HTML database. This article describes to those large enough for HTML data, that can no longer use the SMAQ stack model and those who today may be traditional methods of treatment. In the past, has been the included in the model under mass oriented HTML data Web search engine creators have to face this problem be the processing tools. first to bear the brunt. Today, various social networks, mobile applications and various sensors and scientific fields are II. MAPREDUCE created daily with PB for HTML data. In order to deal with MapReduce is a Google for creating web webpage index the massive data processing challenges for HTML, Google created MapReduce. Google and Yahoo created Hadoop created. MapReduce framework has become today most hatching out of an intact mass oriented HTML data processing mass oriented HTML data processing plant. MapReduce is tools for ecological system. the key, will be oriented in the HTML data collection of a query division, then at multiple nodes to execute in parallel. -
Projects – Other Than Hadoop! Created By:-Samarjit Mahapatra [email protected]
Projects – other than Hadoop! Created By:-Samarjit Mahapatra [email protected] Mostly compatible with Hadoop/HDFS Apache Drill - provides low latency ad-hoc queries to many different data sources, including nested data. Inspired by Google's Dremel, Drill is designed to scale to 10,000 servers and query petabytes of data in seconds. Apache Hama - is a pure BSP (Bulk Synchronous Parallel) computing framework on top of HDFS for massive scientific computations such as matrix, graph and network algorithms. Akka - a toolkit and runtime for building highly concurrent, distributed, and fault tolerant event-driven applications on the JVM. ML-Hadoop - Hadoop implementation of Machine learning algorithms Shark - is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can execute Hive QL queries up to 100 times faster than Hive without any modification to the existing data or queries. Shark supports Hive's query language, metastore, serialization formats, and user-defined functions, providing seamless integration with existing Hive deployments and a familiar, more powerful option for new ones. Apache Crunch - Java library provides a framework for writing, testing, and running MapReduce pipelines. Its goal is to make pipelines that are composed of many user-defined functions simple to write, easy to test, and efficient to run Azkaban - batch workflow job scheduler created at LinkedIn to run their Hadoop Jobs Apache Mesos - is a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It can run Hadoop, MPI, Hypertable, Spark, and other applications on a dynamically shared pool of nodes. -
Learning Key-Value Store Design
Learning Key-Value Store Design Stratos Idreos, Niv Dayan, Wilson Qin, Mali Akmanalp, Sophie Hilgard, Andrew Ross, James Lennon, Varun Jain, Harshita Gupta, David Li, Zichen Zhu Harvard University ABSTRACT We introduce the concept of design continuums for the data Key-Value Stores layout of key-value stores. A design continuum unifies major Machine Databases K V K V … K V distinct data structure designs under the same model. The Table critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as Learning Data Structures fundamentally different data structures to be seen as \views" B-Tree Table of the very same overall design space, and 2) allow \seeing" Graph LSM new data structure designs with performance properties that Store Hash are not feasible by existing designs. The core intuition be- hind the construction of design continuums is that all data Performance structures arise from the very same set of fundamental de- Update sign principles, i.e., a small set of data layout design con- Data Trade-offs cepts out of which we can synthesize any design that exists Access Patterns in the literature as well as new ones. We show how to con- Hardware struct, evaluate, and expand, design continuums and we also Cloud costs present the first continuum that unifies major data structure Read Memory designs, i.e., B+tree, Btree, LSM-tree, and LSH-table. Figure 1: From performance trade-offs to data structures, The practical benefit of a design continuum is that it cre- key-value stores and rich applications. -
Database Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
Riak KV Performance in Sensor Data Storage Application
ISSN 2079-3316 PROGRAM SYSTEMS: THEORY AND APPLICATIONS no.3(34), 2017, pp. 61–85 N. S. Zhivchikova, Y. V. Shevchuk Riak KV performance in sensor data storage application Abstract. A sensor data storage system is an important part of data analysis systems. The duty of sensor data storage is to accept time series data from remote sources, store them and provide access to retrospective data on demand. As the number of sensors grows, the storage system scaling becomes a concern. In this article we experimentally evaluate Riak KV|a scalable distributed key-value data store as a backend for a sensor data storage system. Key words and phrases: sensor data, write performance, distributed storage, time series, Riak, Erlang. 2010 Mathematics Subject Classification: 68M20 Introduction The purpose of a sensor data storage is to store data coming in small portions from a large number of sensors. The data should be stored so as to facilitate efficient retrieval of a (possibly large) data array by sensor identifier and time interval, e.g. to draw a graph. The system is described by three parameters: the number of sensors S, incoming data rate in megabytes per second A, and the storage period Y . In single-computer implementation there are limits on S, A, Y that can't be achieved without computer upgrade to non-commodity hardware which involves disproportional system cost increase. When the system design goals exceed the S, A, Y limits reachable by a single commodity computer, a viable solution is to move to distributed architecture | using multiple commodity computers to meet the design goals. -
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Working with MIG • Our robust technology has been used by major broadcasters and media clients for over 7 years • Voting, Polling and Real-time Interactivity through second screen solutions • Incremental revenue generating services integrated with TV productions • Facilitate 10,000+ interactions per second as standard across our platforms • Platform and services have been audited by Deloitte and other compliant bodies • High capacity throughput for interactions, voting and transactions on a global scale • Partner of choice for BBC, ITV, Channel 5, SKY, MTV, Endemol, Fremantle and more: 1 | © 2012 Mobile Interactive Group @ QCON London mVoy Products High volume mobile messaging campaigns & mobile payments Social Interactivity & Voting via Facebook, iPhone, Android & Web Create, build, host & manage mobile commerce, mobile sites & apps Interactive messaging & multi-step marketing campaigns 2 | © 2012 Mobile Interactive Group @ QCON London MIG Technologies • Erlang • RIAK & leveldb • Redis • Ubuntu • Ruby on Rails • Java • Node.js • MongoDB • MySQL 3 | © 2012 Mobile Interactive Group @ QCON London Battle Stories • Building a wallet • Optimizing your hardware stack • Building a robust queue 4 | © 2012 Mobile Interactive Group @ QCON London Building a wallet • Fast – Over 10,000 debits / sec ( votes ) – Over 1,000 credits / sec • Scalable – Double hardware == Double performance • Robust / Recoverable – Transactions can not be lost – Wallet balances recoverable in the event of multi-server failure • Auditable – Complete transaction history -
Big Data: a Survey the New Paradigms, Methodologies and Tools
Big Data: A Survey The New Paradigms, Methodologies and Tools Enrico Giacinto Caldarola1,2 and Antonio Maria Rinaldi1,3 1Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Napoli, Italy 2Institute of Industrial Technologies and Automation, National Research Council, Bari, Italy 3IKNOS-LAB Intelligent and Knowledge Systems, University of Naples Federico II, LUPT 80134, via Toledo, 402-Napoli, Italy Keywords: Big Data, NoSQL, NewSQL, Big Data Analytics, Data Management, Map-reduce. Abstract: For several years we are living in the era of information. Since any human activity is carried out by means of information technologies and tends to be digitized, it produces a humongous stack of data that becomes more and more attractive to different stakeholders such as data scientists, entrepreneurs or just privates. All of them are interested in the possibility to gain a deep understanding about people and things, by accurately and wisely analyzing the gold mine of data they produce. The reason for such interest derives from the competitive advantage and the increase in revenues expected from this deep understanding. In order to help analysts in revealing the insights hidden behind data, new paradigms, methodologies and tools have emerged in the last years. There has been a great explosion of technological solutions that arises the need for a review of the current state of the art in the Big Data technologies scenario. Thus, after a characterization of the new paradigm under study, this work aims at surveying the most spread technologies under the Big Data umbrella, throughout a qualitative analysis of their characterizing features. -
Your Data Always Available for Applications and Users
DATASHEET RIAK® KV ENTERPRISE YOUR DATA ALWAYS AVAILABLE FOR APPLICATIONS AND USERS Companies rely on data to power their day-to- day operations. It is imperative that this data be always available. Even minutes of application RIAK KV BENEFITS downtime can mean lost sales, a poor user experience, and a bruised brand. This can add up GLOBAL AVAILABILITY to millions in lost revenue. Most databases work A distributed database with advanced local and multi-cluster at small scale, but how do you scale out, up, and replication means your data is always available. down predictably and linearly as your data grows? MASSIVE SCALABILITY You need a different database. Basho Riak® KV Automatic data distribution in the cluster and the ease of adding Enterprise is a distributed NoSQL database nodes mean near-linear performance increase as your data grows. architected to meet your application needs. Riak KV provides high availability and massive OPERATIONAL SIMPLICITY scalability. Riak KV can be operationalized at lower Easy to run, easy to add nodes to your cluster. Operations are costs than traditional relational databases and is powerful yet simple. Ensure your operations team sleeps better. easy to manage at scale. FAULT TOLERANCE Riak KV integrates with Apache Spark, Redis A masterless, multi-node architecture ensures no data loss in the event Caching, Apache Solr, and Apache Mesos of network or hardware failures. to reduce the complexity of integrating and deploying other Big Data technologies. FAST DATA ACCESS Your users expect your application to be fast. Low latency means your data requests are served predictably even during peak times. -
“Consistent Hashing”
Recap: Configurations R/W Configuration Kind of Consistency W=N and R=1 Read optimized strong consistency. W=1 and R=N Write optimized strong consistency. W+R<=N Eventual consistency. Read might miss recent writes. W+R>N Strong consistency. Read will see at least one most recent write. Consistency Levels • Is there something between the extreme configurations “strong consistency” and “eventual consistency”? • Consider a client is working with a key value store Recap: Distributed Setup • N copies per record/object, spread across servers Client node4 node1 node2 node… node … node3 Client-Centric Consistency and Seen Writes Client-Centric Consistency: provides guarantees for a single client concerning the consistency of the accesses to a data store by that client. A client reading a value for a key is seeing a subset of the writes to this key; given the past history of writes by itself and other clients. Client-Centric Read Consistency Guarantees Guarantee Explanation Strong Consistency See all previous writes. Eventual Consistency See (any) subset of previous writes. Consistent Prefix See initial sequence of writes. Bounded Staleness See all “old” writes. E.g., everything older than 10 minutes. Monotonic Reads See increasing subset of writes. Read My Writes See all writes performed by reader. Causal Consistency • Consistency issues…. Our dog, Charlie, ran away today. Can’t find him, Alice we are afraid he got overrun by a car! Posted at 9:30am Thank God! I am so glad to hear this! Bob Posted at 10:20am Causal Consistency (2) • How it was supposed to appear…. Our dog, Charlie, ran away today. -
Memcached, Redis, and Aerospike Key-Value Stores Empirical
Memcached, Redis, and Aerospike Key-Value Stores Empirical Comparison Anthony Anthony Yaganti Naga Malleswara Rao University of Waterloo University of Waterloo 200 University Ave W 200 University Ave W Waterloo, ON, Canada Waterloo, ON, Canada +1 (226) 808-9489 +1 (226) 505-5900 [email protected] [email protected] ABSTRACT project. Thus, the results are somewhat biased as the tested DB The popularity of NoSQL database and the availability of larger setup might be set to give more advantage of one of the systems. DRAM have generated a great interest in in-memory key-value We will discuss more in Section 8 (Related Work). stores (kv-store) in the recent years. As a consequence, many In this work, we conduct a thorough experimental evaluation by similar kv-store store projects/products has emerged. Besides the comparing three major key-value stores nowadays, namely Redis, benchmarking results provided by the KV-store developers which Memcached, and Aerospike. We first elaborate the databases that are usually tested under their favorable setups and scenario, there we tested in Section 3. Then, the evaluation methodology are very limited comprehensive resources for users to decide comprises experimental setups, i.e., single and cluster mode; which kv-store to choose given a specific workload/use-case. To benchmark setups including the description of YCSB, dataset provide users with an unbiased and up-to-date insight in selecting configurations, types workloads (i.e., read-heavy, balanced, in-memory kv-stores, we conduct a study to empirically compare write-heavy), and concurrent access; and evaluation metrics will Redis, Memcached and Aerospike on equal ground by trying to be discussed in Section 4.