NOSQL Databases
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NOSQL databases can be categorized into 4 types: key-value stores, column family/big table clones, document databases, and graph databases. Key-value stores store data as key-value pairs and allow retrieval by key, column family databases organize data by rows and columns with variable schemas, document databases store data as documents with key-value attribute pairs, and graph databases store data as nodes and relationships. The document discusses the characteristics of each type and examples like MongoDB, Cassandra, and Neo4j.
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NoSQL databases were developed to handle large volumes of data and optimize for read/write operations better than relational databases. Popular NoSQL databases include MongoDB, Cassandra, Redis, and Neo4j. MongoDB is a document database where data is stored in collections as documents with dynamic schemas. Redis is an in-memory key-value store that also supports data structures like lists, sets, and hashes. Both MongoDB and Redis can scale horizontally and offer high availability through replication.
Datastores
The document provides an overview of high performance scalable data stores, also known as NoSQL systems, that have been introduced to provide faster indexed data storage than relational databases. It discusses key-value stores, document stores, extensible record stores, and relational databases that provide horizontal scaling. The document contrasts several popular NoSQL systems, including Redis, Scalaris, Tokyo Tyrant, Voldemort, Riak, and SimpleDB, focusing on their data models, features, performance, and tradeoffs between consistency and scalability.
No sql databases
an introduction to No sql term recently used.
a map to types and different architectures. a more focused glance on mongo db , cassandra & couch db
Schemaless Databases
The document summarizes the history and evolution of non-relational databases, known as NoSQL databases. It discusses early database systems like MUMPS and IMS, the development of the relational model in the 1970s, and more recent NoSQL databases developed by companies like Google, Amazon, Facebook to handle large, dynamic datasets across many servers. Pioneering systems like Google's Bigtable and Amazon's Dynamo used techniques like distributed indexing, versioning, and eventual consistency that influenced many open-source NoSQL databases today.
No sq lv2
NoSQL databases are non-relational data storage systems that are designed for large volumes of data across many servers. They are schema-less, support document or key-value data models, and are distributed, open source, and designed for scalability. Common types include key-value stores, document databases, column-family stores, and graph databases. NoSQL databases sacrifice consistency guarantees and transactions for horizontal scalability and high availability.
NoSQL powerpoint presentation difference with rdbms
NoSQL powerpoint presentation difference with rdbms
The state of KOS in the Linked Data movement
- The publishing, management, and interoperating of KOS for the Semantic Web.
Content: 1. Value vocabularies in the Linked Data Hub – CKAN The Data Hub
2. Semantic assets registries
2a. Asset Description Metadata Schema (ADMS)
2b. DCMI Application Profile for KOS Resource
3. Thesaurus data model (ISO 25964) and alignment with SKOS
(presented at ASIS&T 2012 Annual Conference)
The Cassandra Distributed Database
This document summarizes Cassandra, an open source distributed database management system designed to handle large amounts of data across many commodity servers. It discusses Cassandra's history, key features like tunable consistency levels and support for structured and indexed columns. Case studies describe how companies like Digg, Twitter, Facebook and Mahalo use Cassandra to handle terabytes of data and high transaction volumes. The roadmap outlines upcoming releases that will improve features like compaction, management tools, and support for dynamic schema changes.
Learning Cassandra NoSQL
Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple data-centres,with asynchronous master-less replication allowing low latency operations for all clients.
Big Data technology Landscape
The document provides an overview of Big Data technology landscape, specifically focusing on NoSQL databases and Hadoop. It defines NoSQL as a non-relational database used for dealing with big data. It describes four main types of NoSQL databases - key-value stores, document databases, column-oriented databases, and graph databases - and provides examples of databases that fall under each type. It also discusses why NoSQL and Hadoop are useful technologies for storing and processing big data, how they work, and how companies are using them.
NOsql Presentation.pdf
The document provides an introduction to NoSQL databases, including key definitions and characteristics. It discusses that NoSQL databases are non-relational and do not follow RDBMS principles. It also summarizes different types of NoSQL databases like document stores, key-value stores, and column-oriented stores. Examples of popular databases for each type are also provided.
Deep dive to ElasticSearch - معرفی ابزار جستجوی الاستیکی
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NoSQL - Cassandra & MongoDB.pptx
This document provides an overview of NoSQL databases Cassandra and MongoDB. It begins with an introduction to RDBMS and discusses the need for NoSQL databases in terms of handling big data. Key concepts covered include the CAP theorem, data models of Cassandra and MongoDB, replication, and automatic failover. The document concludes by emphasizing the usefulness of NoSQL for availability and processing unstructured data at scale.
JavaCro'15 - Elasticsearch as a search alternative to a relational database -...
JavaCro'15 - Elasticsearch as a search alternative to a relational database -...HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association
Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene. It allows storing and searching of documents in near real-time. Documents are stored in indexes which can be sharded across multiple nodes for horizontal scalability and high availability. Queries use a simple JSON over HTTP interface to retrieve and analyze documents. The PBZ bank uses Elasticsearch to index over 600 million documents from customer transactions for fast retrieval of turnovers by account number.Elasticsearch as a search alternative to a relational database
The volume of data that we are working with is growing every day, the size of data is pushing us to find new intelligent solutions for problem’s put in front of us. Elasticsearch server has proved it self as an excellent full text search solution for big volume’s of data.
Similar to NOSQL Databases (20)
NoSQL powerpoint presentation difference with rdbms
NoSQL powerpoint presentation difference with rdbms
Deep dive to ElasticSearch - معرفی ابزار جستجوی الاستیکی
Deep dive to ElasticSearch - معرفی ابزار جستجوی الاستیکی
JavaCro'15 - Elasticsearch as a search alternative to a relational database -...
JavaCro'15 - Elasticsearch as a search alternative to a relational database -...
Elasticsearch as a search alternative to a relational database
Elasticsearch as a search alternative to a relational database
NOSQL Databases
- 1. NOSQL Databases Not Only SQL Databases
- 2. 4 NOSQL Categories 1)Key-Value stores 2)Column Family/Big Table Clones 3)Document Databases 4)Graph Databases
- 3. 1)Key-Value Stores Key Value Key ValueKey Value Key Value Key Value Store1 Store2 Store3 Store4 Store5 Distributed Hash-Table Ex: Riak, Voldemork, Tokyo The Value can be anything and is accessed by providing a key(Allow you to store metadata)
- 4. 2)Column Family/Big Table Clones Every row has its own schemas Ex: HBase,HyperTable,Cassandra
- 5. 3)Document Databases Key/Value Pairs Collection Key/Value Pairs Collection Key/Value Pairs Collection Key/Value Pairs Collection Ex:CouchDB,MongoDB JSON Object
- 6. 1 2 3 Nodes of records (Entities) Relationships(between these records) Name : Hicham Age : 23 Type: Knows Time: 4 years Properties(Key/Value Pairs Collections) on both Nodes and Relationships 4)Graph Databases
- 7. Data Size Data Complexity 1)Key-Value Stores 2)Column Family/Big Table Clones 4)Graph Databases 3)Document Databases NOSQL Data Models