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This document discusses NoSQL databases and MongoDB. It describes the different types of NoSQL databases, including key-value stores, column family stores, document databases and graph databases. It then provides details about MongoDB, including that it is a document-oriented NoSQL database that is schema-free and uses collections to organize data. It explains basic CRUD operations in MongoDB like create, read, update and delete. It concludes with details about MongoDB's funding and number of employees.
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Slides from workshop held on 12/14 in Asbury Park, NJ
http://www.meetup.com/Jersey-Shore-Tech/events/148118762/?gj=ro2_e&a=ro2_gnl&rv=ro2_e&_af_eid=148118762&_af=event
Considerations for using NoSQL technology on your next IT project - Akmal Cha...
Over the past few years, we have seen the emergence and growth of NoSQL technology. This has attracted interest from organizations looking to solve new business problems. There are also examples of how this technology has been used to bring practical and commercial benefits to some organizations. However, since it is still an emerging technology, careful consideration is required in finding the relevant developer skills and choosing the right product. This presentation will discuss these issues in greater detail. In particular, it will focus on some of the leading NoSQL products, such as MongoDB, Cassandra, Redis, and Neo4j and will discuss their architectures and suitability for different problems. Short demonstrations, using Java, are planned to give the audience a feel for the practical aspects of such products.
Operational Analytics Using Spark and NoSQL Data Stores
NoSQL data stores have emerged for scalable capture and real-time analysis of data. Apache Spark and Hadoop provide additional scalable analytics processing. This session looks at these technologies and how they can be used to support operational analytics to improve operational effectiveness. It also looks at an example of how operational analytics can be implemented in NoSQL environments using the Basho Data Platform with Apache Spark:
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•NoSQL Use Cases
•The need for operational analytics
•Types of operational analysis
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Introduction to NoSQL
The document provides an introduction and overview of NoSQL databases. It discusses why NoSQL databases were created, the different categories of NoSQL databases including column stores, document stores, and key-value stores. It also provides an overview of Hadoop, describing it as a framework that allows distributed processing of large datasets across computer clusters.
UNIT-2.pptx
NoSQL is a non-relational database approach that accommodates a wide variety of data models. It is non-relational, distributed, flexible and scalable. The four main types of NoSQL databases are document databases, key-value stores, column-oriented databases, and graph databases. MongoDB is an example of a document-oriented NoSQL database. NoSQL databases offer benefits over relational databases like flexible schemas, horizontal scalability, and fast queries. Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its parallel programming model and the Hadoop Distributed File System for storage.
Mongo DB: Operational Big Data Database
MongoDB is the leading NoSQL database due to a plenitude of reasons, open source, general purpose, document oriented database supported by a large community and educational platform. It's horizontal scalability features allows this to fit in the operational big data scenarios where the business needs point to realtime analytics and ever-increasing data sets. This talk will focus on the usage of MongoDB for big data operational purposes and why it's ideal to be used in such scenarios. Also integration with other notable big data technology out there like Hadoop and BI tools.
Norberto Leite - Senior Solutions Architect, @MongoDB.
Mongo DB presentation during the Pentaho & Big Data Ecosystem - Live Seminar 2013
Mongo Internal Training session by Soner Altin
Database management systems first appeared in the 1960s as computers grew more powerful. Navigational databases in the 1960s allowed only sequential processing. Edgar Codd suggested the relational model in the 1970s, allowing users to search databases through integration of navigational and other models. A relational database organizes data in tables based on relations and uses SQL for querying.
Everything You Need to Know About MongoDB Development.pptx
Today, organizations from different verticals want to harness the power of data to grab new business opportunities and touch new heights of success. Such an urge leads them to follow unique ways to use and handle data effectively. After all, the right use of data boosts the ability to make business decisions faster. But at the same time, working with data is not as easy as a walk in the garden. It sometimes turns out to be a long-standing problem for businesses that also affects their overall functioning.
Companies expect fast phase development and better data management in every scenario. Modern web-based applications development demands a quality working system that can be deployed faster, and the application is able to scale in the future as per the constantly changing environment.
Earlier, relational databases were used as a primary data store for web application development. But today, developers show a high interest in adopting alternative data stores for modern applications such as NoSQL (Not Only Structured Query Language) because of its incredible benefits. And if you ask us, one of the technologies that can do wonders in modern web-based application development is MongoDB.
MongoDB is the first name strike in our heads when developing scalable applications with evolving data schemas. Because MongoDB is a document database, it makes it easier for developers to store both structured and unstructured data. Stores and handles large amounts of data quickly, MongoDB is undoubtedly the smart move toward building scalable and data-driven applications. If you’re wondering what MongoDB is and how it can help your digital success, this blog is surely for you.
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Mongo db
- 1. www.sti-innsbruck.at© Copyright 2013 STI INNSBRUCK www.sti-innsbruck.at
- 2. www.sti-innsbruck.at Outline 2 • Family of NoSQL DBs • MongoDB profile • Basic operations • Company details
- 3. www.sti-innsbruck.at The family of NoSQL DBs 3 • Key-values Stores – Hash table where there is a unique key and a pointer to a particular item of data. – Focus on scaling to huge amounts of data – E.g. Oracle BDB • Column Family Stores – To store and process very large amounts of data distributed over many machines – E.g. Cassandra, HBase • Document Databases – Collections of Key-Value collections – The next level of Key/value, allowing nested values associated with each key. – Appropriate for Web apps. – E.g. CouchDB, MongoDb • Graph Databases – Bases on property-graph model – Appropriate for Social networking, Recommendations – E.g. Neo4J, Infinite Graph
- 4. www.sti-innsbruck.at MongoDB profile 4 • Document-oriented NoSQL database. • Schema-free. • Based on Binary JSON; BSON[2]. • Organized in Group of Documents Collections – Informal namespacing • Auto-Sharding in order to scale horizontally. • Simple query language. Rich, document-based queries. • Map/Reduce support (See more at [7]). • Open Source (GNU AGPL v3.0.)
- 6. www.sti-innsbruck.at CRUD operations - create 6 ©MongoDB [4] SQL MongoDB Insert a new user.
- 7. www.sti-innsbruck.at CRUD operations – create (cont’d) 7 ©MongoDB [3]
- 8. www.sti-innsbruck.at CRUD operations - read 8 ©MongoDB [3] Find the users of age greater than 18 and sort by age.
- 9. www.sti-innsbruck.at CRUD operations - update 9 ©MongoDB [4] SQL MongoDB Update the users of age greater than 18 by setting the status field to A.
- 10. www.sti-innsbruck.at CRUD operations - delete 10 ©MongoDB [4] SQL MongoDB Delete the users with status equal to D.
- 11. www.sti-innsbruck.at Company details 11 - MongoDB is funded by leading investment firms and technology companies, including Altimeter Capital, Fidelity Investments, Flybridge Capital Partners, In- Q-Tel, Intel Capital, NEA, Red Hat, Salesforce.com, Sequoia Capital, Union Square Ventures and T. Rowe Price. [5] - October 4th , 2013 – $150 million in funding With more than $231 million in total investment since the company’s inception in 2007, MongoDB is now the best-funded Big Data technology. MongoDB’s financing marks the largest single funding round for any database vendor, NoSQL or otherwise. [6] - 320 employees
- 12. www.sti-innsbruck.at References [1] Mikayel Vardanyan, Picking the right NoSQL Database Tool: http://blog.monitis.com/index.php/2011/05/22/picking-the-right-nosql-database-tool/ [2] BSON Specification: http://bsonspec.org/ [3] MongoDB CRUD operations: http://docs.mongodb.org/manual/crud/ [4] MongoDB Write operations: http://docs.mongodb.org/manual/core/write-operations/ [5] MongoDB Investors: http://www.mongodb.com/investors [6] MongoDB Closes $150 Million in Funding: http://www.mongodb.com/press/mongodb- closes-150-million-funding [7] MongoDB Aggregation introduction: http://docs.mongodb.org/manual/core/aggregation-introduction/ 12