From Nosql Accumulo to Newsql Graphulo: Design and Utility of Graph Algorithms Inside a Bigtable Database
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Oracle Vs. Nosql Vs. Newsql Comparing Database Technology
Oracle vs. NoSQL vs. NewSQL Comparing Database Technology John Ryan Senior Solution Architect, Snowflake Computing Table of Contents The World has Changed . 1 What’s Changed? . 2 What’s the Problem? . .. 3 Performance vs. Availability and Durability . 3 Consistecy vs. Availability . 4 Flexibility vs . Scalability . 5 ACID vs. Eventual Consistency . 6 The OLTP Database Reimagined . 7 Achieving the Impossible! . .. 8 NewSQL Database Technology . 9 VoltDB . 10 MemSQL . 11 Which Applications Need NewSQL Technology? . 12 Conclusion . 13 About the Author . 13 ii The World has Changed The world has changed massively in the past 20 years. Back in the year 2000, a few million users connected to the web using a 56k modem attached to a PC, and Amazon only sold books. Now billions of people are using to their smartphone or tablet 24x7 to buy just about everything, and they’re interacting with Facebook, Twitter and Instagram. The pace has been unstoppable . Expectations have also changed. If a web page doesn’t refresh within seconds we’re quickly frustrated, and go elsewhere. If a web site is down, we fear it’s the end of civilisation as we know it. If a major site is down, it makes global headlines. Instant gratification takes too long! — Ladawn Clare-Panton Aside: If you’re not a seasoned Database Architect, you may want to start with my previous articles on Scalability and Database Architecture. Oracle vs. NoSQL vs. NewSQL eBook 1 What’s Changed? The above leads to a few observations: • Scalability — With potentially explosive traffic growth, IT systems need to quickly grow to meet exponential numbers of transactions • High Availability — IT systems must run 24x7, and be resilient to failure. -
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. -
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 -
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. -
A Big Data Roadmap for the DB2 Professional
A Big Data Roadmap For the DB2 Professional Sponsored by: align http://www.datavail.com Craig S. Mullins Mullins Consulting, Inc. http://www.MullinsConsulting.com © 2014 Mullins Consulting, Inc. Author This presentation was prepared by: Craig S. Mullins President & Principal Consultant Mullins Consulting, Inc. 15 Coventry Ct Sugar Land, TX 77479 Tel: 281-494-6153 Fax: 281.491.0637 Skype: cs.mullins E-mail: [email protected] http://www.mullinsconsultinginc.com This document is protected under the copyright laws of the United States and other countries as an unpublished work. This document contains information that is proprietary and confidential to Mullins Consulting, Inc., which shall not be disclosed outside or duplicated, used, or disclosed in whole or in part for any purpose other than as approved by Mullins Consulting, Inc. Any use or disclosure in whole or in part of this information without the express written permission of Mullins Consulting, Inc. is prohibited. © 2014 Craig S. Mullins and Mullins Consulting, Inc. (Unpublished). All rights reserved. © 2014 Mullins Consulting, Inc. 2 Agenda Uncover the roadmap to Big Data… the terminology and technology used, use cases, and trends. • Gain a working knowledge and definition of Big Data (beyond the simple three V's definition) • Break down and understand the often confusing terminology within the realm of Big Data (e.g. polyglot persistence) • Examine the four predominant NoSQL database systems used in Big Data implementations (graph, key/value, column, and document) • Learn some of the major differences between Big Data/NoSQL implementations vis-a-vis traditional transaction processing • Discover the primary use cases for Big Data and NoSQL versus relational databases © 2014 Mullins Consulting, Inc. -
Big Data Solutions and Applications
P. Bellini, M. Di Claudio, P. Nesi, N. Rauch Slides from: M. Di Claudio, N. Rauch Big Data Solutions and applications Slide del corso: Sistemi Collaborativi e di Protezione Corso di Laurea Magistrale in Ingegneria 2013/2014 http://www.disit.dinfo.unifi.it Distributed Data Intelligence and Technology Lab 1 Related to the following chapter in the book: • P. Bellini, M. Di Claudio, P. Nesi, N. Rauch, "Tassonomy and Review of Big Data Solutions Navigation", in "Big Data Computing", Ed. Rajendra Akerkar, Western Norway Research Institute, Norway, Chapman and Hall/CRC press, ISBN 978‐1‐ 46‐657837‐1, eBook: 978‐1‐ 46‐657838‐8, july 2013, in press. http://www.tmrfindia.org/b igdata.html 2 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 3 Index 6. Access/Data Rendering aspects 7. Data Analysis and Mining/Ingestion Aspects 8. Comparison and Analysis of Architectural Features • Data Management • Data Access and Visual Rendering • Mining/Ingestion • Data Analysis • Architecture 4 Part 1 The Big Data Problem. 5 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 6 What is Big Data? Variety Volume Value 5V’s of Big Data Velocity Variability 7 5V’s of Big Data (1/2) • Volume: Companies amassing terabytes/petabytes of information, and they always look for faster, more efficient, and lower‐ cost solutions for data management. -
How to Store and Process Big Data: Are Today’S Databases Sufficient? Jaroslav Pokorný
How to Store and Process Big Data: Are Today’s Databases Sufficient? Jaroslav Pokorný To cite this version: Jaroslav Pokorný. How to Store and Process Big Data: Are Today’s Databases Sufficient?. 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Nov 2014, Ho Chi Minh City, Vietnam. pp.5-10, 10.1007/978-3-662-45237-0_2. hal-01405547 HAL Id: hal-01405547 https://hal.inria.fr/hal-01405547 Submitted on 30 Nov 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License How to store and process Big Data: are today's databases sufficient? Jaroslav Pokorný Department of Software Engineering, Faculty of Mathematics and Physics Charles University, Prague, Czech Republic [email protected] Abstract. The development and extensive use of highly distributed and scalable systems to process Big Data is widely considered. New data management archi- tectures, e.g. distributed file systems and NoSQL databases, are used in this context. On the other hand, features of Big Data like their complexity and data analytics demands indicate that these tools solve Big Data problems only par- tially. -
Top Newsql Databases and Features Classification
International Journal of Database Management Systems ( IJDMS ) Vol.10, No.2, April 2018 TOP NEW SQL DATABASES AND FEATURES CLASSIFICATION Ahmed Almassabi 1, Omar Bawazeer and Salahadin Adam 2 1Department of Computer Science, Najran University, Najran, Saudi Arabia 2Department of Information and Computer Science, King Fahad University of Petroleum and Mineral, Dhahran, Saudi Arabia ABSTRACT Versatility of NewSQL databases is to achieve low latency constrains as well as to reduce cost commodity nodes. Out work emphasize on how big data is addressed through top NewSQL databases considering their features. This NewSQL databases paper conveys some of the top NewSQL databases [54] features collection considering high demand and usage. First part, around 11 NewSQL databases have been investigated for eliciting, comparing and examining their features so that they might assist to observe high hierarchy of NewSQL databases and to reveal their similarities and their differences. Our taxonomy involves four types categories in terms of how NewSQL databases handle, and process big data considering technologies are offered or supported. Advantages and disadvantages are conveyed in this survey for each of NewSQL databases. At second part, we register our findings based on several categories and aspects: first, by our first taxonomy which sees features characteristics are either functional or non-functional. A second taxonomy moved into another aspect regarding data integrity and data manipulation; we found data features classified based on supervised, semi-supervised, or unsupervised. Third taxonomy was about how diverse each single NewSQL database can deal with different types of databases. Surprisingly, Not only do NewSQL databases process regular (raw) data, but also they are stringent enough to afford diverse type of data such as historical and vertical distributed system, real-time, streaming, and timestamp databases. -
Comparison of Sql, Nosql and New Sql Databases in Light of Internet of Things – a Survey
International Journal of Advances in Electronics and Computer Science, ISSN: 2393-2835 Volume-3, Issue-3, Mar.-2016 COMPARISON OF SQL, NOSQL AND NEW SQL DATABASES IN LIGHT OF INTERNET OF THINGS – A SURVEY 1HALEEMUNNISA FATIMA, 2KUMUD WASNIK 1M.Tech (Student), 2Asst. Professor Department of Computer Science and Technology, Usha Mittal Institute of Technology, Mumbai, India E-mail: [email protected], [email protected] Abstract- In this era of technological advancement the recent growth in the IT enabled industry has increased the amount of “Big Data” namely unpredictable amount of data as well as with the arrival of Internet-of-Things, the IoT is expected to generate huge amount of data from varied locations that is collected very quickly. Hence, with features like scalability, consistency and performance factors are coercing the traditional database users into adopting the NoSQL database management system. This new storing database technology is asserted to perform better than the classic SQL counterpart. NoSQL DBMSs although highly scalable compromises on ACID properties for its high performance. To overcome this integral deficiency a modern alternative class of database management system has emerged called the NewSQL. NewSQL extends the scalable performance of NoSQL system at the same time retaining the SQL and ACID behavior of the traditional database system. In this paper the challenges in the said technology which both NoSQL and NewSQL have and have to overcome are discussed. Various open source SQL, NoSQL and NewSQL databases are studied such as MySOL, MongoDB, Cassandra and VoltDB which further contains data models, characteristics and comparisons. Keywords- SQL, NoSQL, NewSQL, Document Stores, Column Family, ACID, Big Data, Internet of Things. -
Trends in Development of Databases and Blockchain
Trends in Development of Databases and Blockchain Mayank Raikwar∗, Danilo Gligoroski∗, Goran Velinov† ∗ Norwegian University of Science and Technology (NTNU) Trondheim, Norway † University Ss. Cyril and Methodius Skopje, Macedonia Email: {mayank.raikwar,danilog}@ntnu.no, goran.velinov@finki.ukim.mk Abstract—This work is about the mutual influence between some features which traditional database has. Blockchain can two technologies: Databases and Blockchain. It addresses two leverage the traditional database features by either integrat- questions: 1. How the database technology has influenced the ing the traditional database with blockchain or, to create a development of blockchain technology?, and 2. How blockchain technology has influenced the introduction of new functionalities blockchain-oriented distributed database. The inclusion of the in some modern databases? For the first question, we explain how database features will leverage the blockchain with low la- database technology contributes to blockchain technology by un- tency, high throughput, fast scalability, and complex queries on locking different features such as ACID (Atomicity, Consistency, blockchain data. Thus having the features of both blockchain Isolation, and Durability) transactional consistency, rich queries, and database, the application enhances its efficiency and real-time analytics, and low latency. We explain how the CAP (Consistency, Availability, Partition tolerance) theorem known security. Many of the blockchain platforms are now integrating for databases influenced -
Effectiveness of Nosql and Newsql Databases in Mobile Network Event Data: Cassandra and Parstream/Kinetic
© 2018 by the authors; licensee RonPub, Lübeck, Germany. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). Open Access Open Journal of Databases (OJDB) Volume 5, Issue 1, 2018 www.ronpub.com/ojdb ISSN 2199-3459 Effectiveness of NoSQL and NewSQL Databases in Mobile Network Event Data: Cassandra and ParStream/Kinetic Petri Kotiranta, Marko Junkkari Faculty of Natural Sciences, Computer Sciences, University of Tampere, FI-33014, Finland, [email protected], [email protected] ABSTRACT Continuously growing amount of data has inspired seeking more and more efficient database solutions for storing and manipulating data. In big data sets, NoSQL databases have been established as alternatives for traditional SQL databases. The effectiveness of these databases has been widely tested, but the tests focused only on key-value data that is structurally very simple. Many application domains, such as telecommunication, involve more complex data structures. Huge amount of Mobile Network Event (MNE) data is produced by an increasing number of mobile and ubiquitous applications. MNE data is structurally predetermined and typically contains a large number of columns. Applications that handle MNE data are usually insert intensive, as a huge amount of data are generated during rush hours. NoSQL provides high scalability and its column family stores suits MNE data well, but NoSQL does not support ACID features of the traditional relational databases. NewSQL is a new kind of databases, which provide the high scalability of NoSQL while still maintaining ACID guarantees of the traditional DBMS.