What is RDF and what is it good for? How RDF Databases Differ from Other NoSQL Solutions - The Datagraph Blog. This started out as an answer at Semantic Overflow on how RDF database systems differ from other currently available NoSQL solutions.
I've here expanded the answer somewhat and added some general-audience context. RDF database systems are the only standardized NoSQL solutions available at the moment, being built on a simple, uniform data model and a powerful, declarative query language. These systems offer data portability and toolchain interoperability among the dozens of competing implementations that are available at present, avoiding any need to bet the farm on a particular product or vendor.
In case you're not familiar with the term, NoSQL ("Not only SQL") is a loosely-defined umbrella moniker for describing the new generation of non-relational database systems that have sprung up in the last several years. These systems tend to be inherently distributed, schema-less, and horizontally scalable. RDF database systems form the largest subset of this last NoSQL category. Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase comparison. (Yes it's a long title, since people kept asking me to write about this and that too :) I do when it has a point.)
While SQL databases are insanely useful tools, their monopoly in the last decades is coming to an end. And it's just time: I can't even count the things that were forced into relational databases, but never really fitted them. (That being said, relational databases will always be the best for the stuff that has relations.) But, the differences between NoSQL databases are much bigger than ever was between one SQL database and another. This means that it is a bigger responsibility on software architects to choose the appropriate one for a project right at the beginning. In this light, here is a comparison of Cassandra, Mongodb, CouchDB, Redis, Riak, Couchbase (ex-Membase), Hypertable, ElasticSearch, Accumulo, VoltDB, Kyoto Tycoon, Scalaris, OrientDB, Aerospike, Neo4j and HBase: The most popular ones Redis (V3.0RC) For example: To store real-time stock prices.
MongoDB (2.6.7) Elasticsearch - - Open Source, Distributed, RESTful, Search Engine. Visual Representation of SQL Joins. Introduction This is just a simple article visually explaining SQL JOINs.
Background I'm a pretty visual person. Things seem to make more sense as a picture. I looked all over the Internet for a good graphical representation of SQL JOINs, but I couldn't find any to my liking. Using the code I am going to discuss seven different ways you can return data from two relational tables. For the sake of this article, I'll refer to 5, 6, and 7 as LEFT EXCLUDING JOIN, RIGHT EXCLUDING JOIN, and OUTER EXCLUDING JOIN, respectively. Inner JOIN This is the simplest, most understood Join and is the most common. SELECT <select_list> FROM Table_A A INNER JOIN Table_B B ON A.Key = B.Key Left JOIN This query will return all of the records in the left table (table A) regardless if any of those records have a match in the right table (table B). SELECT <select_list>FROM Table_A A LEFT JOIN Table_B B ON A.Key = B.Key Right JOIN SELECT <select_list>FROM Table_A A RIGHT JOIN Table_B B ON A.Key = B.Key Outer JOIN Examples.