NoSQL

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NoSQL is a trend which has evolved into a loosely organized movement based on the fact that SQL (designed in the 1960s-1970s) is limited in function for today's modern applications and is based on an outdated view of hardware where computing power was much lower and storage per system was much, much smaller and significantly more expensive than modern hardware. This is mostly thanks to Moore's Law which states that computing power will double every year.

NoSQL especially addresses the issue of Metcalfe's Law and the Viral effects present in many modern Web Applications, where traffic to Web Servers tends to be sporadic and bursty in nature, spiking when certain pieces of content are published, or, when an old item of content is shared via a Social Network. It is sometimes referred to as schemaless design or schemaless database because of its lack of a traditional relational database structure where tables are mapped to one another via forieng keys and a specific itempotency and/or reciprocity (i.e. 1:1, 1:n, n:1, n:n).


Datastores

Hierarchical

HDF 5


Document

existDB

"XML document-store" driven database solution.

CouchDB

Apache CouchDB is a document-oriented database that can be queried and indexed in a MapReduce fashion using JavaScript. CouchDB also offers incremental replication with bi-directional conflict detection and resolution.

MongoDB

MongoDB bridges the gap between key-value stores (which are fast and highly scalable), document stores (which are lightweight) and traditional RDBMS systems (which provide rich queries and deep functionality).

[2][3]

[5] [6]

Key/Value

Hadoop

The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing.


Dynamo

Voldemort
Amazon S3

Amazon Simple Storage Service (Amazon S3).


Cassandra

A highly scalable, eventually consistent, distributed, structured key-value store (used to power several features of Facebook).

Persevere

redis

A persistent key-value database with built-in net interface written in ANSI-C for Posix systems

Oracle KV



Graph

Neo4j

Neo4j is a graph database.

[10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]

4store

4store - An efficient, scalable and stable RDF database: http://4store.org/

Virtuoso

Virtuoso is an Enterprise relational database and tripestore solution.

AllegroGraph

AllegroGraph is an RDF-based triple-store.

Resources


Tutorials


External Links


References

  1. ↑ MongoDB demo (video): http://public.dhe.ibm.com/software/dw/demos/jmongodb/
  2. ↑ How Is Google Analytics So Damn Fast?: http://java.dzone.com/articles/how-google-analytics-so-damn (includes conversion of MySQL to MongoDB query)
  3. ↑ KEEP CALM and QUERY JSON: http://dzone.com/articles/keep-calm-and-query-json
  4. ↑ Crunching 30 Years of NBA Data with MongoDB Aggregation: http://thecodebarbarian.wordpress.com/2014/02/14/crunching-30-years-of-nba-data-with-mongodb-aggregation/
  5. ↑ Why You Should Never Use MongoDB (as a be-all and end-all datastore): http://www.sarahmei.com/blog/2013/11/11/why-you-should-never-use-mongodb/
  6. ↑ MongoDB Basics in 5 Minutes: https://dzone.com/articles/mongodb-basics-in-5-minutes
  7. ↑ If you wagered Bet365 would buy up Basho's remains, you'd be a big winner right now: https://www.theregister.co.uk/2017/08/25/bet365_to_buy_basho_release_code/
  8. ↑ Cassandra installation on Windows 7: http://support.qualityunit.com/249500-Cassandra-installation-on-Windows-7
  9. ↑ NoSQL Quickstart guide: http://docs.oracle.com/cd/NOSQL/html/quickstart.html
  10. ↑ Revisiting Hillary Clinton's email corpus with graph algos and NLP (Part 1 of 3): https://blog.bruggen.com/2019/12/part-13-revisiting-hillary-clintons.html
  11. ↑ Visualizing Breast Cancer Data with Neo4j and GraphXR: https://medium.com/neo4j/visualize-cancer-1c80a95f5bb4
  12. ↑ How to Make "Small Talk" with Your Boss About Music (with the help of graph-based recommendations): https://neo4j.com/blog/make-small-talk-with-your-boss-graph-based-recommendations/
  13. ↑ Analyzing Genomes in a Graph Database: https://medium.com/geekculture/analyzing-genomes-in-a-graph-database-27a45faa0ae8
  14. ↑ Getting Graph Questions Answered through Neo4j Bloom: https://www.youtube.com/watch?v=W7uaSJMNqW4 (examples of similarity & AML "circular vlows" analysis)
  15. ↑ Close to the Edge - Graph Databases through 1970s Prog. Rock: https://grant592.github.io/prog-neo4j/ | VIDEO (exploring GraphDB benefits via RDF imports of Wikidata & SPARQL queries)
  16. ↑ Can Graph Data Science Prove a Movie is "Cursed"? Lets make a graph (out of Wikidata movie articles) and find out!: https://www.youtube.com/watch?v=Cq1bf7ysT6A
  17. ↑ How to Have a Cybersecurity Graph Database on Your PC: https://medium.com/neo4j/how-to-have-a-cybersecurity-graph-database-on-your-pc-366884ac6a08
  18. ↑ Hacking Hacker News for fun and profit : https://blog.arnica.io/hacking-hacker-news-for-fun-and-profit-part-1-41bd6a48a2c2
  19. ↑ Construct a biomedical knowledge graph with NLP: https://towardsdatascience.com/construct-a-biomedical-knowledge-graph-with-nlp-1f25eddc54a0
  20. ↑ Build a Knowledge Graph using NLP & Ontologies tutorial: https://neo4j.com/developer/graph-data-science/build-knowledge-graph-nlp-ontologies/
  21. ↑ Neo4J -- Going Meta series: https://github.com/jbarrasa/goingmeta | VIDEO#1 | VIDEO#2 | VIDEO#3
  22. ↑ Building a Fullstack IMDB Clone with a Java Backend using SparkJava and Neo4j: https://foojay.io/today/building-a-fullstack-imdb-clone-with-a-java-backend-using-sparkjava-and-neo4j/ | SRC | E-LEARNING COURSE
  23. ↑ Schema Design in MongoDB: http://nosql.mypopescu.com/post/907003504/mongodb-schema-design

See Also

Database | SQL | GraphQL | MySQL | DBMS | RDBMS | Linked Data | Ontology | RDF | n3 | Semantic Web | Semantic Search | Cloud Computing | Mahout

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