DOCSLIB.ORG

Unit III: SQL Using R 3.1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Unit III: SQL Using R 3.1 Unit III: SQL using R 3.1. Introduction to NoSQL: Define Nosql Database: NoSQL is originally referring to "non SQL" or "non-relational” and also called "Not only SQL” to emphasize that they may support SQL-like query languages. The RDBMS database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. NoSQL databases are increasingly used in big data and real-time web applications. Benefits of NoSQL Database: No SQL databases are more scalable and provide superior performance. The NoSQL data model addresses several issues that the relational model is not designed to address: • Large volumes of structured, semi-structured, and unstructured data • Agile sprints, quick iteration, and frequent code pushes • Object-oriented programming that is easy to use and flexible • Efficient, scale-out architecture instead of expensive, monolithic architecture Classification of NoSQL databases based on data model: A basic classification based on data model, with examples: Document: Clusterpoint, Apache CouchDB, Couchbase, DocumentDB, HyperDex, Lotus Notes, MarkLogic, MongoDB, OrientDB, Qizx Key-value: CouchDB, Oracle NoSQL Database, Dynamo, FoundationDB, HyperDex, MemcacheDB, Redis, Riak, FairCom c-treeACE, Aerospike, OrientDB, MUMPS Graph: Allegro, Neo4J, InfiniteGraph, OrientDB, Virtuoso, Stardog Multi-model: OrientDB, FoundationDB, ArangoDB, Alchemy Database, CortexDB Differences between SQL database and NoSQL Database: SQL Databases NOSQL Databases Many different types including key-value One type (SQL database) with minor 1. Types stores, document databases, wide-column variations stores, and graph databases Developed in 2000s to deal with limitations of Development Developed in 1970s to deal with first 2. SQL databases, particularly concerning scale, History wave of data storage applications replication and unstructured data storage 3. Examples MySQL, Postgres, Oracle Database MongoDB, Cassandra, HBase, Neo4j Page | 1 Individual records (e.g., "employees") are stored as rows in tables, with each column storing a Varies based on database type. specific piece of data about that For example: record (e.g., "manager," "date hired," key-value stores function similarly to SQL etc.), much like a spreadsheet. databases, but have only two columns Separate data types are stored in ("key" and "value"), with more complex separate tables, and then joined Data Storage information sometimes stored within the 4. together when more complex queries Model "value" columns. are executed. For example, "offices" Document databases do away with the might be stored in one table, and table-and-row model altogether, storing all "employees" in another. When a user relevant data together in single "document" wants to find the work address of an in JSON, XML, or another format, which employee, the database engine joins can nest values hierarchically. the "employee" and "office" tables together to get all the information necessary. Typically dynamic. Records can add new Structure and data types are fixed in information on the fly, and unlike SQL table advance. To store information about rows, dissimilar data can be stored together as 5. Schemas a new data item, the entire database necessary. For some databases (e.g., wide- must be altered, during which time column stores), it is somewhat more the database must be taken offline. challenging to add new fields dynamically. Vertically, meaning a single server must be made increasingly powerful Horizontally, meaning that to add capacity, a in order to deal with increased database administrator can simply add more 6. Scaling demand. It is possible to spread SQL commodity servers or cloud instances. The databases over many servers, but database automatically spreads data across significant additional engineering is servers as necessary. generally required. Mix of open-source (e.g., Postgres, Development 7. MySQL) and closed source (e.g., Open-source Model Oracle Database) Supports Yes, updates can be configured to In certain circumstances and at certain levels 8. Transactions complete entirely or not at all (e.g., document level vs. database level) Page | 2 Specific language using Select, Data Insert, and Update statements, e.g. 9. Through object-oriented APIs Manipulation SELECT fields FROM table WHERE… Depends on product. Some provide strong Can be configured for strong 10. Consistency consistency (e.g., MongoDB) whereas others consistency offer eventual consistency (e.g., Cassandra) Page | 3.
Load more

Recommended publications
  • Combined Documents V2
    Outline: Combining Brainstorming Deliverables Table of Contents 1. Introduction and Definition 2. Reference Architecture and Taxonomy 3. Requirements, Gap Analysis, and Suggested Best Practices 4. Future Directions and Roadmap 5. Security and Privacy - 10 Top Challenges 6. Conclusions and General Advice Appendix A. Terminology Glossary Appendix B. Solutions Glossary Appendix C. Use Case Examples Appendix D. Actors and Roles 1. Introduction and Definition The purpose of this outline is to illustrate how some initial brainstorming documents might be pulled together into an integrated deliverable. The outline will follow the diagram below. Section 1 introduces a definition of Big Data. An extended terminology Glossary is found in Appendix A. In section 2, a Reference Architecture diagram is presented followed by a taxonomy describing and extending the elements of the Reference Architecture. Section 3 maps requirements from use case building blocks to the Reference Architecture. A description of the requirement, a gap analysis, and suggested best practice is included with each mapping. In Section 4 future improvements in Big Data technology are mapped to the Reference Architecture. An initial Technology Roadmap is created on the requirements and gap analysis in Section 3 and the expected future improvements from Section 4. Section 5 is a placeholder for an extended discussion of Security and Privacy. Section 6 gives an example of some general advice. The Appendices provide Big Data terminology and solutions glossaries, Use Case Examples, and some possible Actors and Roles. Big Data Definition - “Big Data refers to the new technologies and applications introduced to handle increasing Volumes of data while enhancing data utilization capabilities such as Variety, Velocity, Variability, Veracity, and Value.” The key attribute is the large Volume of data available that forces horizontal scalability of storage and processing and has implications for all the other V-attributes.
    [Show full text]
  • Oracle Nosql Database
    An Oracle White Paper November 2012 Oracle NoSQL Database Oracle NoSQL Database Table of Contents Introduction ........................................................................................ 2 Technical Overview ............................................................................ 4 Data Model ..................................................................................... 4 API ................................................................................................. 5 Create, Remove, Update, and Delete..................................................... 5 Iteration ................................................................................................... 6 Bulk Operation API ................................................................................. 7 Administration .................................................................................... 7 Architecture ........................................................................................ 8 Implementation ................................................................................... 9 Storage Nodes ............................................................................... 9 Client Driver ................................................................................. 10 Performance ..................................................................................... 11 Conclusion ....................................................................................... 12 1 Oracle NoSQL Database Introduction NoSQL databases
    [Show full text]
  • Oracle® Nosql Database Changelog
    Oracle® NoSQL Database Changelog Release 20.1 E91819-17 July 2020 Oracle NoSQL Database Changelog, Release 20.1 E91819-17 Copyright © 2011, 2020, Oracle and/or its affiliates. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or “commercial computer software documentation” pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, reproduction, duplication, release, display, disclosure, modification, preparation of derivative works, and/or adaptation of i) Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs), ii) Oracle computer documentation and/or iii) other Oracle data, is subject to the rights and limitations specified in the license contained in the applicable contract.
    [Show full text]
  • 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.
    [Show full text]
  • STUDY and SURVEY of BIG DATA for INDUSTRY Surbhi Verma*, Sai Rohit
    ISSN: 2277-9655 [Verma* et al., 5(11): November, 2016] Impact Factor: 4.116 IC™ Value: 3.00 CODEN: IJESS7 IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY STUDY AND SURVEY OF BIG DATA FOR INDUSTRY Surbhi Verma*, Sai Rohit DOI: 10.5281/zenodo.166840 ABSTRACT Now-a-days we rarely observe any company or any industry who don’t have any database. Industries with huge amounts of data are finding it difficult to manage. They all are in search of some technology which can make their work easy and fast. The primary purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data over local data and how they differ with each other. This paper surveys different hardware platforms available for big data and local data and assesses the advantages and drawbacks of each of these platforms. KEYWORDS: Big data, Local data, HadoopBase, Clusterpoint, Mongodb, Couchbase, Database. INTRODUCTION This is an era of Big Data. Big Data is making radical changes in traditional data analysis platforms. To perform any kind of analysis on such huge and complex data, scaling up the hardware platforms becomes imminent and choosing the right hardware/software platforms becomes very important. In this research we are showing how big data has been improvising over the local databases and other technologies. Present day, big data is making a huge turnaround in technological world and so to manage and access data there must be some kind of linking between big data and local data which is not done yet.
    [Show full text]
  • Oracle Nosql Database and Cisco- Collaboration That Produces Results
    Oracle NoSQL Database and Cisco- Collaboration that produces results 1 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. What is Big Data? SOCIAL BLOG SMART METER VOLUME VELOCITY VARIETY VALUE 2 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Why Is It Important? US HEALTH CARE US RETAIL MANUFACTURING GLOBAL PERSONAL EUROPE PUBLIC LOCATION DATA SECTOR ADMIN Increase industry Increase net Decrease dev., Increase service Increase industry value per year by margin by assembly costs by provider revenue by value per year by $300 B 60+% –50% $100 B €250 B “In a big data world, a competitor that fails to sufficiently develop its capabilities will be left behind.” 3 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Source: * McKinsey Global Institute: Big Data – The next frontier for innovation, competition and productivity (May 2011) Big Data in Action DECIDE ACQUIRE Make Better Decisions Using Big Data ANALYZE ORGANIZE 4 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Oracle Integrated Solution Stack DATA VARIETY HDFS HADOOP (MapReduce) In-DB Oracle Loader Mining for HADOOP Oracle NoSQL DB Oracle In-DB Exadata ‘R’ In-DB MapReduce OBIEE Oracle Data Analytics Advanced Oracle Database Integrator INFORMATION DENSITY ACQUIRE ORGANIZE ANALYZE DECIDE 5 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Big Data in Action DECIDE ACQUIRE Acquire all available, schema-based and non- relational data ANALYZE ORGANIZE 6 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Acquiring Big Data Challenge Process high volume, low- Application changes With sub-millisecond density information frequently Velocity from various data-sets 7 Copyright © 2011, Oracle and/or its affiliates.
    [Show full text]
  • 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.
    [Show full text]
  • Oracle Nosql Database EE Data Sheet
    Oracle NoSQL Database 21.1 Enterprise Edition (EE) Oracle NoSQL Database is a multi-model, multi-region, multi-cloud, active-active KEY BUSINESS BENEFITS database, designed to provide a highly-available, scalable, performant, flexible, High throughput and reliable data management solution to meet today’s most demanding Bounded latency workloads. It can be deployed in on-premise data centers and cloud. It is well- Linear scalability suited for high volume and velocity workloads, like Internet of Things, 360- High availability degree customer view, online contextual advertising, fraud detection, mobile Fast and easy deployment application, user personalization, and online gaming. Developers can use a single Smart topology management application interface to quickly build applications that run in on-premise and Online elastic configuration cloud environments. Multi-region data replication Enterprise grade software Applications send network requests against an Oracle NoSQL data store to and support perform database operations. With multi-region tables, data can be globally distributed and automatically replicated in real-time across different regions. Data can be modeled as fixed-schema tables, documents, key-value pairs, and large objects. Different data models interoperate with each other through a single programming interface. Oracle NoSQL Database is a sharded, shared-nothing system which distributes data uniformly across multiple shards in a NoSQL database cluster, based on the hashed value of the primary keys. An Oracle NoSQL Database data store is a collection of storage nodes, each of which hosts one or more replication nodes. Data is automatically populated across these replication nodes by internal replication mechanisms to ensure high availability and rapid failover in the event of a storage node failure.
    [Show full text]
  • An Intelligent Approach for Handling Complexity by Migrating from Conventional Databases to Big Data
    S S symmetry Article An Intelligent Approach for Handling Complexity by Migrating from Conventional Databases to Big Data Shabana Ramzan 1, Imran Sarwar Bajwa 1,* and Rafaqut Kazmi 2 1 Department of Computer Science & IT, Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan; [email protected] 2 School of Computing, University of Technology Malaysia, Johor 81310, Malaysia; [email protected] * Correspondence: [email protected] Received: 26 October 2018; Accepted: 14 November 2018; Published: 3 December 2018 Abstract: Handling complexity in the data of information systems has emerged into a serious challenge in recent times. The typical relational databases have limited ability to manage the discrete and heterogenous nature of modern data. Additionally, the complexity of data in relational databases is so high that the efficient retrieval of information has become a bottleneck in traditional information systems. On the side, Big Data has emerged into a decent solution for heterogenous and complex data (structured, semi-structured and unstructured data) by providing architectural support to handle complex data and by providing a tool-kit for efficient analysis of complex data. For the organizations that are sticking to relational databases and are facing the challenge of handling complex data, they need to migrate their data to a Big Data solution to get benefits such as horizontal scalability, real-time interaction, handling high volume data, etc. However, such migration from relational databases to Big Data is in itself a challenge due to the complexity of data. In this paper, we introduce a novel approach that handles complexity of automatic transformation of existing relational database (MySQL) into a Big data solution (Oracle NoSQL).
    [Show full text]
  • Oracle Big Data Appliance X8-2
    ORACLE DATA SHEET Oracle Big Data Appliance X8-2 Oracle Big Data Appliance is a flexible, high-performance, secure platform for running diverse workloads on Hadoop, Kafka and Spark. With Oracle Big Data SQL, Oracle Big Data Appliance extends Oracle’s industry-leading implementation of SQL to Hadoop/NoSQL and Kafka systems. By combining the newest technologies from the Hadoop ecosystem and powerful Oracle SQL capabilities together on a single pre-configured platform, Oracle Big Data Appliance is uniquely capable to support rapid development of new Big Data applications and tight integration with existing relational data. Oracle Big Data Appliance X8-2 Oracle Big Data Appliance is an open, multi-purpose engineered system for Hadoop and Spark workloads and streaming data processing. Big Data Appliance is designed to run diverse workloads – from Hadoop-only workloads (Yarn, Spark, Hive etc.) to interactive, all-encompassing interactive SQL queries using Oracle Big Data SQL across Apache Kafka, Hadoop and NoSQL databases. Big Data Appliance, is a Cloudera Certified platform and supports both Cloudera 5.x and Cloudera 6.x. Big Data Appliance provides an open environment for innovation while maintaining tight integration and enterprise-level support. Organizations can deploy external software to KEY FEATURES support new functionality – such as graph analytics, natural language processing and • Massively scalable, open infrastructure to store, analyze and fraud detection. Support for non-Oracle components is delivered by their respective manage big data support channels and not by Oracle. • Industry-leading security, performance and the most Lower TCO and Faster Time to Value comprehensive big data tool set on the market all bundled in an easy to Big Data Appliance provides unique pricing to offer both a lower initial deployment cost deploy appliance as well as a dramatically reduced three and four-year TCO when compared to a Do-It- • Flexible configuration and elastic Yourself Hadoop, Spark or Kafka system.
    [Show full text]
  • Multimodel Database with Ora
    Disclaimer The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. MULTIMODEL DATABASE WITH ORACLE DATABASE 18C Table of Contents Introduction 1 Multimodel Database Architecture 2 Multimodel Database Features in Oracle 18c 3 JSON in Oracle Database 5 Graph Database and Analytics in Oracle Spatial and Graph 6 Property Graph Features in Oracle Spatial and Graph 6 RDF Semantic Graph Triple Store Features in Oracle Spatial and Graph 7 Spatial Database and Analytics in Oracle Spatial and Graph 7 Sharded Database Model 8 Oracle XML DB 9 Oracle Text 10 Oracle SecureFiles 10 Storage Optimization in SecureFiles 10 SecureFiles Features in Oracle Database 18c 11 Conclusion 12 0 | MULTIMODEL DATABASE WITH ORACLE DATABASE 18C Introduction Over the nearly 40 years in the evolution of commercial relational database management systems, a consistent pattern has emerged as the capabilities, data types, analytics, and data models have been developed and adopted. With each new generation of computing architecture – from centralized mainframe, to client server, to internet computing, to the Cloud – new generations of data management systems have been developed to address new applications, workloads and workflows. Today, the successful operation of corporations, enterprises, and other organizations relies on the management, understanding and efficient use of vast amounts of unstructured Big Data that may come from social media, web content, sensors and machine output, and documents.
    [Show full text]
  • Scalable Storage: the Drive for Web-Scale Data Management
    Scalable Storage: The drive for web-scale data management Bryan Rosander University of Central Florida [email protected] March 28, 2012 Abstract Data-intensive applications have become prevalent in todays information econ- omy. The sheer amount of data stored and utilized by todays web services presents unique challenges in the areas of scalability, security, and availability. This has opened new possibilities in data mining, allowing for more tightly integrated, in- formative services. It has also created new challenges. Traditional, monolithic, relational databases are inherently limited in terms of scalability. This has caused many leading companies to abandon traditional databases in favor of horizontally scalable data stores. This paper will evaluate the state of the art in data stor- age and retrieval, covering the history of the database and moving on to newer database technologies such as Googles Bigtable, Apache Cassandra, and Amazons DynamoDB. 1 Introduction Data storage and retrieval has become a central part of many popular web applications. As the amount of data available increases, the database capacity must scale up to meet it. Traditional methods of scaling up database capacity focus mainly on increasing the computing power of the single server on which the database resides. This strategy has been sufficient for many applications but has become infeasible for those that need to store more data than can be efficiently processed by one machine. Newer database paradigms emphasizing horizontal scalability, the ability to add as many nodes as are necessary and redistribute the data between all active nodes, have been growing in popularity. This increase in scalability does come at a cost.
    [Show full text]