DOCSLIB.ORG

SQL Vs Nosql

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
SQL Vs Nosql Comparative Study of SQL and NoSQL Ruihan Wang; Zongyan Yang University of Rochester Introduction NoSQL and Non-Relational Databases Our result can also be supported Yishan Li and Sathiamoorthy Manoharan found that while NoSQL databases are generally good at storing key-value data. They also observed that in NoSQL • For the discussion of our paper, we will narrow the SQL vs • By the middle of the 2000’s, relational database supremacy was database, different types of operation will lead to various NoSQL topic down to the comparison and performance just about to end. In particular, the difference in application performance [2]. Although not all NoSQL databases perform better advantage of NoSQL over SQL based on models and theorems, architectures between the client-server era and the era of than SQL database in terms of speed, NoSQL still have advantages together with the limitations that result from those factors. massive web-scale applications created pressures on the generally. • SQL stands for Structured Query Language, invented as a relational database that could not be relieved through standard high-level interface for most of databases, usually used incremental innovation Conclusions as DDL and DML for the management of relational database • Relational databases use ACID transactions to handle management system (RDBMS). Databases based on the consistency across the whole database. However, this inherently relational model include MySQL, MS-SQL Server, Oracle clashes with a cluster environment, so NoSQL databases offer a In a word, it‘s hard to simply say SQL is better than NoSQL, or database and so on, each of them supports SQL as the query range of options for consistency and distribution. The common vice versa. Both SQL and NoSQL have advantages and language. characteristics of NoSQL databases are: relational-model free, disadvantages under different situations. As for speed, NoSQL is • NoSQL, which stands for Not Only SQL, however is a non- running well on clusters, open-sourced, and the most important generally faster than SQL, especially for key-value storage in our relational database management system. Leading NoSQL one, schema less. Speed Comparisons experiment; while SQL generally has much stronger consistency databases include MongoDB, Cassandra, CouchDB, HBase, etc. • NoSQL databases operate without a schema, allowing freely check that keep all data valid. With the current popularity of “Big Data”, NoSQL databases adding fields to database records without having to define any We did the benchmark comparison of reading and writing speed SQL databases, for the reason that they are relation oriented, have were pioneered and improved a lot by top internet companies changes in structure first, which is particularly useful when between MySQL, Oracle and MongoDB from advantages of vertical scalability and strong consistency. As like Amazon, Google and LinkedIn. dealing with non-uniform data and custom fields which forced stssoft.com/benchmarks, developed by STS SOFT Company. consistency is prioritized in SQL databases, the database manage • The main difference between non-relational data model and the relational databases to use names like custom field tables that The machine we tested on has the following configurations: system has to do lots of work to maintain the consistent state, are awkward to process and understand. which will definitely compromise the performance. It's also traditional one is, the non-relational model is designed for • OS: Microsoft Windows 10 Pro 64 bit processing huge amount of data in a second, with relatively low expansive to add new columns in SQL systems. NoSQL databases, • CPU: Intel (R) Core (TM) i5-3210M CPU @ 2.50GHz as they are designed to be flexible and fast, have less constrains consistency requirement. As a consequence, it relaxes the ACID General Comparisons constrains provided by many relational database systems, in • RAM: DDR3 8GB 1333MHz than SQL. As a result, they can reduce the overhead of consistency exchange for the improvement of performance. • Storage: SAMSUNG MZ7PC128HAFU-000H1 checking and process faster. Besides, NoSQL store data as objects • NoSQL is like a movement rather than a technology. Relational • Database Settings: InsertsPerQuery, 5000; Indexing (documents or key-value pair), which means it can be store data databases can not be replaced and are the most common form Technology, BTree; Randomness, 100% distributed. On the other hand, NoSQL database may not fully SQL and Relational Databases of database in use. The familiarity, stability, feature set, and support ACID transactions, which may result data inconsistency. available support are compelling arguments for most We gathered data like average speed and number of records, got projects.[3] The change is that now we see relational databases the results from below. We could see NoSQL, especially Oracle ACID properties: In order to maintain consistency in a relational as just one option for data storage. has a big speed advantage over MySQL. References database, before and after transaction, certain properties are followed. These are called ACID properties, standing for • Normalization can help manage data in an efficient way at the cost of the performance of data processing, because RDBMS atomicity, consistency, isolation, durability. [1] International Journal of Advanced Research in Computer has to meet the constraint of ACID. NoSQL requires a lower • The atomicity property requires that each transaction be "all or Science and Software Engineering, ‘SQL and NoSQL Databases’ degree of normalization than RDBMS and offers an alternative nothing": if one part of the transaction fails, then the entire by Vatika Sharma, Meenu Dave. by eliminating schemas at the expense of relaxing ACID transaction fails, and the database state is left unchanged. principles to satisfy the speed, flexibility, and distribute [2] IEEE Pacific RIM Conference on Communications, • The consistency property ensures that any transaction will bring requirement. The following chart shows how the same data Computers, and Signal Processing ‘ A performance comparison of the database from one valid state to another. Any data written to appears within schema in SQL and without schema in NoSQL SQL and NoSQL databases’ by Li Yishan, Manoharan the database must be valid according to all defined rules looks like: Sathiamoorthy including constraints, cascades, triggers and any combination [3] ‘Comparative Study of SQL & NoSQL Databases’ by Supriya thereof. S. Pore, Swalaya B. Pawar • The isolation property ensures that the concurrent execution of [4] Rick Cattell. 2011. Scalable SQL and NoSQL data stores. transactions results in a system state that would be obtained if SIGMOD Rec. 39, 4 (May 2011), 12-27. transactions were executed sequentially. [5] Han J, Haihong E, Le G, et al. Survey on NoSQL database[C] • The durability property ensures that once a transaction has been Figure 2. Reading Speed vs Records Pervasive computing and applications (ICPCA), 2011 6th committed, it will remain so, even in the event of power loss, international conference on. IEEE, 2011: 363-366. crashes, or errors. [6] S. H. Aboutorabi, M. Rezapour, M. Moradi and N. Ghadiri, “Perfor- mance evaluation of SQL and MongoDB databases for big The ACID properties, in totality, provide a mechanism to ensure e-commerce data,” 2015 International Symposium on Computer correctness and consistency of a database in a way such that each Science and Software Engineering (CSSE), Tabriz, 2015, pp. 1-7. transaction is a group of operations that acts a single unit, produces consistent results, acts in isolation from other operations and Acknowledgement and Contact updates that it makes are durably stored. Ruihan Wang, Computer Science Department The following table gives a rough description of those differences [email protected] and we will go in deep and specifically to focus on the performance of speed of SQL and NoSQL. Zongyan Yang, Data Science Department Figure 3. Writing Speed vs Records [email protected] RESEARCH POSTER PRESENTATION DESIGN © 2015 www.PosterPresentations.com.
Load more

Recommended publications
  • Not ACID, Not BASE, but SALT a Transaction Processing Perspective on Blockchains
    Not ACID, not BASE, but SALT A Transaction Processing Perspective on Blockchains Stefan Tai, Jacob Eberhardt and Markus Klems Information Systems Engineering, Technische Universitat¨ Berlin fst, je, [email protected] Keywords: SALT, blockchain, decentralized, ACID, BASE, transaction processing Abstract: Traditional ACID transactions, typically supported by relational database management systems, emphasize database consistency. BASE provides a model that trades some consistency for availability, and is typically favored by cloud systems and NoSQL data stores. With the increasing popularity of blockchain technology, another alternative to both ACID and BASE is introduced: SALT. In this keynote paper, we present SALT as a model to explain blockchains and their use in application architecture. We take both, a transaction and a transaction processing systems perspective on the SALT model. From a transactions perspective, SALT is about Sequential, Agreed-on, Ledgered, and Tamper-resistant transaction processing. From a systems perspec- tive, SALT is about decentralized transaction processing systems being Symmetric, Admin-free, Ledgered and Time-consensual. We discuss the importance of these dual perspectives, both, when comparing SALT with ACID and BASE, and when engineering blockchain-based applications. We expect the next-generation of decentralized transactional applications to leverage combinations of all three transaction models. 1 INTRODUCTION against. Using the admittedly contrived acronym of SALT, we characterize blockchain-based transactions There is a common belief that blockchains have the – from a transactions perspective – as Sequential, potential to fundamentally disrupt entire industries. Agreed, Ledgered, and Tamper-resistant, and – from Whether we are talking about financial services, the a systems perspective – as Symmetric, Admin-free, sharing economy, the Internet of Things, or future en- Ledgered, and Time-consensual.
    [Show full text]
  • Failures in DBMS
    Chapter 11 Database Recovery 1 Failures in DBMS Two common kinds of failures StSystem filfailure (t)(e.g. power outage) ‒ affects all transactions currently in progress but does not physically damage the data (soft crash) Media failures (e.g. Head crash on the disk) ‒ damagg()e to the database (hard crash) ‒ need backup data Recoveryyp scheme responsible for handling failures and restoring database to consistent state 2 Recovery Recovering the database itself Recovery algorithm has two parts ‒ Actions taken during normal operation to ensure system can recover from failure (e.g., backup, log file) ‒ Actions taken after a failure to restore database to consistent state We will discuss (briefly) ‒ Transactions/Transaction recovery ‒ System Recovery 3 Transactions A database is updated by processing transactions that result in changes to one or more records. A user’s program may carry out many operations on the data retrieved from the database, but the DBMS is only concerned with data read/written from/to the database. The DBMS’s abstract view of a user program is a sequence of transactions (reads and writes). To understand database recovery, we must first understand the concept of transaction integrity. 4 Transactions A transaction is considered a logical unit of work ‒ START Statement: BEGIN TRANSACTION ‒ END Statement: COMMIT ‒ Execution errors: ROLLBACK Assume we want to transfer $100 from one bank (A) account to another (B): UPDATE Account_A SET Balance= Balance -100; UPDATE Account_B SET Balance= Balance +100; We want these two operations to appear as a single atomic action 5 Transactions We want these two operations to appear as a single atomic action ‒ To avoid inconsistent states of the database in-between the two updates ‒ And obviously we cannot allow the first UPDATE to be executed and the second not or vice versa.
    [Show full text]
  • MDA Process to Extract the Data Model from a Document-Oriented Nosql Database Amal Ait Brahim, Rabah Tighilt Ferhat, Gilles Zurfluh
    MDA process to extract the data model from a document-oriented NoSQL database Amal Ait Brahim, Rabah Tighilt Ferhat, Gilles Zurfluh To cite this version: Amal Ait Brahim, Rabah Tighilt Ferhat, Gilles Zurfluh. MDA process to extract the data model from a document-oriented NoSQL database. ICEIS 2019: 21st International Conference on Enterprise Information Systems, May 2019, Heraklion, Greece. pp.141-148, 10.5220/0007676201410148. hal- 02886696 HAL Id: hal-02886696 https://hal.archives-ouvertes.fr/hal-02886696 Submitted on 1 Jul 2020 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. Open Archive Toulouse Archive Ouverte OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible This is an author’s version published in: https://oatao.univ-toulouse.fr/26232 Official URL : https://doi.org/10.5220/0007676201410148 To cite this version: Ait Brahim, Amal and Tighilt Ferhat, Rabah and Zurfluh, Gilles MDA process to extract the data model from a document-oriented NoSQL database. (2019) In: ICEIS 2019: 21st International Conference on Enterprise Information Systems, 3 May 2019 - 5 May 2019 (Heraklion, Greece).
    [Show full text]
  • MDA Process to Extract the Data Model from Document-Oriented Nosql Database
    MDA Process to Extract the Data Model from Document-oriented NoSQL Database Amal Ait Brahim, Rabah Tighilt Ferhat and Gilles Zurfluh Toulouse Institute of Computer Science Research (IRIT), Toulouse Capitole University, Toulouse, France Keywords: Big Data, NoSQL, Model Extraction, Schema Less, MDA, QVT. Abstract: In recent years, the need to use NoSQL systems to store and exploit big data has been steadily increasing. Most of these systems are characterized by the property "schema less" which means absence of the data model when creating a database. This property brings an undeniable flexibility by allowing the evolution of the model during the exploitation of the base. However, query expression requires a precise knowledge of the data model. In this article, we propose a process to automatically extract the physical model from a document- oriented NoSQL database. To do this, we use the Model Driven Architecture (MDA) that provides a formal framework for automatic model transformation. From a NoSQL database, we propose formal transformation rules with QVT to generate the physical model. An experimentation of the extraction process was performed on the case of a medical application. 1 INTRODUCTION however that it is absent in some systems such as Cassandra and Riak TS. The "schema less" property Recently, there has been an explosion of data offers undeniable flexibility by allowing the model to generated and accumulated by more and more evolve easily. For example, the addition of new numerous and diversified computing devices. attributes in an existing line is done without Databases thus constituted are designated by the modifying the other lines of the same type previously expression "Big Data" and are characterized by the stored; something that is not possible with relational so-called "3V" rule (Chen, 2014).
    [Show full text]
  • What Is Nosql? the Only Thing That All Nosql Solutions Providers Generally Agree on Is That the Term “Nosql” Isn’T Perfect, but It Is Catchy
    NoSQL GREG SYSADMINBURD Greg Burd is a Developer Choosing between databases used to boil down to examining the differences Advocate for Basho between the available commercial and open source relational databases . The term Technologies, makers of Riak. “database” had become synonymous with SQL, and for a while not much else came Before Basho, Greg spent close to being a viable solution for data storage . But recently there has been a shift nearly ten years as the product manager for in the database landscape . When considering options for data storage, there is a Berkeley DB at Sleepycat Software and then new game in town: NoSQL databases . In this article I’ll introduce this new cat- at Oracle. Previously, Greg worked for NeXT egory of databases, examine where they came from and what they are good for, and Computer, Sun Microsystems, and KnowNow. help you understand whether you, too, should be considering a NoSQL solution in Greg has long been an avid supporter of open place of, or in addition to, your RDBMS database . source software. [email protected] What Is NoSQL? The only thing that all NoSQL solutions providers generally agree on is that the term “NoSQL” isn’t perfect, but it is catchy . Most agree that the “no” stands for “not only”—an admission that the goal is not to reject SQL but, rather, to compensate for the technical limitations shared by the majority of relational database implemen- tations . In fact, NoSQL is more a rejection of a particular software and hardware architecture for databases than of any single technology, language, or product .
    [Show full text]
  • Nosql Database Technology
    NoSQL Database Technology Post-relational data management for interactive software systems NOSQL DATABASE TECHNOLOGY Table of Contents Summary 3 Interactive software has changed 4 Users – 4 Applications – 5 Infrastructure – 5 Application architecture has changed 6 Database architecture has not kept pace 7 Tactics to extend the useful scope of RDBMS technology 8 Sharding – 8 Denormalizing – 9 Distributed caching – 10 “NoSQL” database technologies 11 Mobile application data synchronization 13 Open source and commercial NoSQL database technologies 14 2 © 2011 COUCHBASE ALL RIGHTS RESERVED. WWW.COUCHBASE.COM NOSQL DATABASE TECHNOLOGY Summary Interactive software (software with which a person iteratively interacts in real time) has changed in fundamental ways over the last 35 years. The “online” systems of the 1970s have, through a series of intermediate transformations, evolved into today’s web and mobile appli- cations. These systems solve new problems for potentially vastly larger user populations, and they execute atop a computing infrastructure that has changed even more radically over the years. The architecture of these software systems has likewise transformed. A modern web ap- plication can support millions of concurrent users by spreading load across a collection of application servers behind a load balancer. Changes in application behavior can be rolled out incrementally without requiring application downtime by gradually replacing the software on individual servers. Adjustments to application capacity are easily made by changing the number of application servers. But database technology has not kept pace. Relational database technology, invented in the 1970s and still in widespread use today, was optimized for the applications, users and inf- rastructure of that era.
    [Show full text]
  • SQL Vs Nosql: a Performance Comparison
    SQL vs NoSQL: A Performance Comparison Ruihan Wang Zongyan Yang University of Rochester University of Rochester [email protected] [email protected] Abstract 2. ACID Properties and CAP Theorem We always hear some statements like ‘SQL is outdated’, 2.1. ACID Properties ‘This is the world of NoSQL’, ‘SQL is still used a lot by We need to refer the ACID properties[12]: most of companies.’ Which one is accurate? Has NoSQL completely replace SQL? Or is NoSQL just a hype? SQL Atomicity (Structured Query Language) is a standard query language A transaction is an atomic unit of processing; it should for relational database management system. The most popu- either be performed in its entirety or not performed at lar types of RDBMS(Relational Database Management Sys- all. tems) like Oracle, MySQL, SQL Server, uses SQL as their Consistency preservation standard database query language.[3] NoSQL means Not A transaction should be consistency preserving, meaning Only SQL, which is a collection of non-relational data stor- that if it is completely executed from beginning to end age systems. The important character of NoSQL is that it re- without interference from other transactions, it should laxes one or more of the ACID properties for a better perfor- take the database from one consistent state to another. mance in desired fields. Some of the NOSQL databases most Isolation companies using are Cassandra, CouchDB, Hadoop Hbase, A transaction should appear as though it is being exe- MongoDB. In this paper, we’ll outline the general differences cuted in iso- lation from other transactions, even though between the SQL and NoSQL, discuss if Relational Database many transactions are execut- ing concurrently.
    [Show full text]
  • APPENDIX G Acid Dissociation Constants
    harxxxxx_App-G.qxd 3/8/10 1:34 PM Page AP11 APPENDIX G Acid Dissociation Constants §␮ ϭ 0.1 M 0 ؍ (Ionic strength (␮ † ‡ † Name Structure* pKa Ka pKa ϫ Ϫ5 Acetic acid CH3CO2H 4.756 1.75 10 4.56 (ethanoic acid) N ϩ H3 ϫ Ϫ3 Alanine CHCH3 2.344 (CO2H) 4.53 10 2.33 ϫ Ϫ10 9.868 (NH3) 1.36 10 9.71 CO2H ϩ Ϫ5 Aminobenzene NH3 4.601 2.51 ϫ 10 4.64 (aniline) ϪO SNϩ Ϫ4 4-Aminobenzenesulfonic acid 3 H3 3.232 5.86 ϫ 10 3.01 (sulfanilic acid) ϩ NH3 ϫ Ϫ3 2-Aminobenzoic acid 2.08 (CO2H) 8.3 10 2.01 ϫ Ϫ5 (anthranilic acid) 4.96 (NH3) 1.10 10 4.78 CO2H ϩ 2-Aminoethanethiol HSCH2CH2NH3 —— 8.21 (SH) (2-mercaptoethylamine) —— 10.73 (NH3) ϩ ϫ Ϫ10 2-Aminoethanol HOCH2CH2NH3 9.498 3.18 10 9.52 (ethanolamine) O H ϫ Ϫ5 4.70 (NH3) (20°) 2.0 10 4.74 2-Aminophenol Ϫ 9.97 (OH) (20°) 1.05 ϫ 10 10 9.87 ϩ NH3 ϩ ϫ Ϫ10 Ammonia NH4 9.245 5.69 10 9.26 N ϩ H3 N ϩ H2 ϫ Ϫ2 1.823 (CO2H) 1.50 10 2.03 CHCH CH CH NHC ϫ Ϫ9 Arginine 2 2 2 8.991 (NH3) 1.02 10 9.00 NH —— (NH2) —— (12.1) CO2H 2 O Ϫ 2.24 5.8 ϫ 10 3 2.15 Ϫ Arsenic acid HO As OH 6.96 1.10 ϫ 10 7 6.65 Ϫ (hydrogen arsenate) (11.50) 3.2 ϫ 10 12 (11.18) OH ϫ Ϫ10 Arsenious acid As(OH)3 9.29 5.1 10 9.14 (hydrogen arsenite) N ϩ O H3 Asparagine CHCH2CNH2 —— —— 2.16 (CO2H) —— —— 8.73 (NH3) CO2H *Each acid is written in its protonated form.
    [Show full text]
  • IBM Cloudant: Database As a Service Fundamentals
    Front cover IBM Cloudant: Database as a Service Fundamentals Understand the basics of NoSQL document data stores Access and work with the Cloudant API Work programmatically with Cloudant data Christopher Bienko Marina Greenstein Stephen E Holt Richard T Phillips ibm.com/redbooks Redpaper Contents Notices . 5 Trademarks . 6 Preface . 7 Authors. 7 Now you can become a published author, too! . 8 Comments welcome. 8 Stay connected to IBM Redbooks . 9 Chapter 1. Survey of the database landscape . 1 1.1 The fundamentals and evolution of relational databases . 2 1.1.1 The relational model . 2 1.1.2 The CAP Theorem . 4 1.2 The NoSQL paradigm . 5 1.2.1 ACID versus BASE systems . 7 1.2.2 What is NoSQL? . 8 1.2.3 NoSQL database landscape . 9 1.2.4 JSON and schema-less model . 11 Chapter 2. Build more, grow more, and sleep more with IBM Cloudant . 13 2.1 Business value . 15 2.2 Solution overview . 16 2.3 Solution architecture . 18 2.4 Usage scenarios . 20 2.5 Intuitively interact with data using Cloudant Dashboard . 21 2.5.1 Editing JSON documents using Cloudant Dashboard . 22 2.5.2 Configuring access permissions and replication jobs within Cloudant Dashboard. 24 2.6 A continuum of services working together on cloud . 25 2.6.1 Provisioning an analytics warehouse with IBM dashDB . 26 2.6.2 Data refinement services on-premises. 30 2.6.3 Data refinement services on the cloud. 30 2.6.4 Hybrid clouds: Data refinement across on/off–premises. 31 Chapter 3.
    [Show full text]
  • Drugs and Acid Dissociation Constants Ionisation of Drug Molecules Most Drugs Ionise in Aqueous Solution.1 They Are Weak Acids Or Weak Bases
    Drugs and acid dissociation constants Ionisation of drug molecules Most drugs ionise in aqueous solution.1 They are weak acids or weak bases. Those that are weak acids ionise in water to give acidic solutions while those that are weak bases ionise to give basic solutions. Drug molecules that are weak acids Drug molecules that are weak bases where, HA = acid (the drug molecule) where, B = base (the drug molecule) H2O = base H2O = acid A− = conjugate base (the drug anion) OH− = conjugate base (the drug anion) + + H3O = conjugate acid BH = conjugate acid Acid dissociation constant, Ka For a drug molecule that is a weak acid The equilibrium constant for this ionisation is given by the equation + − where [H3O ], [A ], [HA] and [H2O] are the concentrations at equilibrium. In a dilute solution the concentration of water is to all intents and purposes constant. So the equation is simplified to: where Ka is the acid dissociation constant for the weak acid + + Also, H3O is often written simply as H and the equation for Ka is usually written as: Values for Ka are extremely small and, therefore, pKa values are given (similar to the reason pH is used rather than [H+]. The relationship between pKa and pH is given by the Henderson–Hasselbalch equation: or This relationship is important when determining pKa values from pH measurements. Base dissociation constant, Kb For a drug molecule that is a weak base: 1 Ionisation of drug molecules. 1 Following the same logic as for deriving Ka, base dissociation constant, Kb, is given by: and Ionisation of water Water ionises very slightly.
    [Show full text]
  • Top 5 Considerations When Evaluating Nosql Databases February 2015 Table of Contents
    A MongoDB White Paper Top 5 Considerations When Evaluating NoSQL Databases February 2015 Table of Contents Introduction 1 Data Model 2 Document Model 2 Graph Model 2 Key-Value and Wide Column Models 2 Query Model 3 Document Database 3 Graph Database 3 Key Value and Wide Column Databases 3 Consistency Model 4 Consistent Systems 4 Eventually Consistent Systems 4 APIs 4 Idiomatic Drivers 5 Thrift or RESTful APIs 5 Pluggable Storage API 5 Commercial Support and Community Strength 5 Commercial Support 5 Community Strength 5 Conclusion 6 We Can Help 6 Introduction Relational databases have a long-standing position in most Development teams have a strong say in the technology organizations, and for good reason. Relational databases selection process. This community tends to find that the underpin legacy applications that meet current business relational data model is not well aligned with the needs of needs; they are supported by an extensive ecosystem of their applications. Consider: tools; and there is a large pool of labor qualified to • Developers are working with new data types — implement and maintain these systems. structured, semi-structured, unstructured and But companies are increasingly considering alternatives to polymorphic data — and massive volumes of it. legacy relational infrastructure. In some cases the • Long gone is the twelve-to-eighteen month waterfall motivation is technical — such as a need to scale or development cycle. Now small teams work in agile perform beyond the capabilities of their existing systems — sprints, iterating quickly and pushing code every week while in other cases companies are driven by the desire to or two, even every day.
    [Show full text]
  • Nosql Database Evaluation Guide How Leading Nosql Databases Compare Across the Eight Core Requirements TABLE of CONTENTS Introduction
    NoSQL Database Evaluation Guide How Leading NoSQL Databases Compare Across the Eight Core Requirements TABLE OF CONTENTS Introduction Introduction 2 Digital Economy Overview 3 The business world is undergoing massive change as industry after industry shifts to the Digital Economy. It’s an economy powered by the Internet and other 21st-century technologies — the Data Access 5 cloud, mobile, social media, and big data. At the heart of every Digital Economy business are its web, mobile, and Internet of Things (IoT) applications: they’re the primary way companies interact Performance 6 with customers today, and how companies run more and more of their business. The experiences Scalability 7 that companies deliver via those apps largely determine how satisfied — and how loyal — customers will be. Availability 8 Building and running these web, mobile, and IoT applications has created a new set of technology Geographic 9 requirements. Enterprise architecture needs to be far more agile than ever before, and requires Distribution an approach to real-time data management that can accommodate unprecedented levels of scale, speed, and data flexibility. Relational databases are unable to meet these new requirements, and Big Data Integration 10 enterprises are therefore turning to NoSQL database technology. Administration 11 Three Phases of NoSQL Evolution and Adoption: From Grassroots to Mainstream Mobile 12 Enterprise adoption of NoSQL has unfolded in three overlapping phases. In phase I (which Conclusion 12 started around 2010), developers required flexibility to support the agile development of proof Checklist 13 of concepts and small applications. In phase II (which began around 2013), enterprises required performance, scalability, and availability to develop and/or migrate targeted mission-critical services.
    [Show full text]