This document introduces NoSQL databases. It defines NoSQL as lacking SQL and sometimes meaning "Not only SQL". It explains that NoSQL is used for big data and caching, and includes key-value, columnar, document, and graph databases. Each type is described, with examples like Redis, Cassandra, MongoDB, and Neo4J. The document advises using NoSQL for large, unstructured data when consistency does not immediately matter, and relational databases for smaller, more structured data that requires ACID properties.

1. NoSQL
AN INTRODUCTION

2. Who am I?
 Graduate student here
 Worked with a lot of web development
 Have used a few of the tools professionally before

3. What is NoSQL?
 Literally that – lack of SQL
 Sometimes Not only SQL

4. What is SQL then?
 Structured Query Language
 Used to work with (R)DBMS

5. That doesn’t answer the question
 NoSQL is a generic term for many data stores
 Unstructured, schema-less, relation-less

6. Where are the used?
 Big Data!!!
 Little Data aka Caching
 Unstructured document stores

7. Types of NoSQL
 In memory - Key/Value based
 Redis/MemcacheD
 Column Based
 Cassandra, HBase
 Document Based
 MongoDB, CouchDB
 Graph Based
 Neo4J

8. Key/Value Based
 Basically an in-memory hash table
 Has some sort of availability for handling collections/lists/sets
 Can be used as a cache (MemcacheD)
 Can be used for high-performance data lookups (Redis)
 Pub/Sub

9. Column Based
 Compromise between the simplicity of key/value and tables
 Records are grouped by one or more keys or columns
 Good for storing large amounts of data with large amounts of
unstructured information
 Twitter uses this!

10. Document Based
 Allows deep nesting of keys a values – which is a document
 A document can contain more documents
 Generally uses a format like JSON
 Good for append only data or infrequently updated data (like
analytics)
 Great for aggregating and analyzing (big) data – achieved through
distributed eventually consistency
 DO NOT USE THIS INSTEAD OF AN RDBMS

11. Graph Based
 Nodes to store data and edges to store relations between them
 Mathematically powerful – can be used to induce interesting
hypotheses from data
 Recommendation/search engines
 Ironically more relational than RDBMS

12. When NOT to use NoSQL
 When you need ACID (Atomicity, Consistency, Isolation, Durability)
 Which is almost always
 When your data is updated frequently
 When you’re not sure if you want to use SQL or not

13. When to use NoSQL
 In conjunction with RDBMS – caching
 Consistency does not (immediately) matter
 When you want to distribute/replicate large-scale data
 When you have HUGE data
 When you need the speed (only if you know what you’re doing)
 When you have no idea how your data will be structured
 That’s when a RDBMS approach will mean a bunch of joins

14. Thank you!