NoSQL Databases and Analytic Use Cases
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This document discusses NoSQL databases and their use for analytics. It begins by defining NoSQL as non-relational databases that trade traditional relational features for simplicity and scalability. It then covers different types of NoSQL databases and the trades they make versus relational databases. The document also discusses how NoSQL databases allow for distributed, geographically distributed data and different consistency models. It proposes using NoSQL databases for operational data storage and analytics by leveraging their flexibility, scalability and ability to handle large, sparse datasets through techniques like MapReduce transformations.
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NoSQL Databases and Analytic Use Cases
- 1. NoSQL Databases and Analytic Use Cases Aaron Cordova INFORMS
- 2. NoSQL • Perhaps better is “Non-Relational” • Departure from conventional relational db • Trade traditional features for simplicity, scalability, flexibility
- 3. Types of NoSQL DBs Columnar! ! BigTable Hbase Accumulo Cassandra Graph! ! Neo4j OrientDB Key-Value ! Dynamo Riak Voldemort BerkeleyDB Document! ! MongoDB CouchDB MarkLogic (XML)
- 4. Trades Give up! ! Cross-row Transactions Relational JOINS Type Checking SQL Gain! ! Simplicity Scalability (distributed) Schema Flexibility Geographic distribution Programmatic APIs
- 5. NoSQL Distributed Name Age Phone Bob 43 555-1212 Jenny 32 555-1213 Sally 28 555-1214 Joe 45 555-1215 Up to Petabytes
- 6. Consistency Name Age Phone Bob 43 555-1212 Jenny 32 555-1213 Sally 28 555-1214 Joe 45 555-1215 Name Age Phone Bob 43 555-1212 Jenny 32 867-5309 Sally 28 555-1214 Joe 45 555-1215 Name Age Phone Bob 43 555-1212 Jenny 32 555-1213 Sally 28 555-1214 Joe 45 555-1215 X Multiple Data Centers Single Data Center
- 7. Consistency Geographically Distributed, ! Eventually Consistent! ! Dynamo Riak Voldemort Cassandra MongoDB CouchDB Single Data Center, Highly Consistent! ! BigTable Hbase Accumulo Cassandra Neo4j OrientDB MongoDB MarkLogic (XML)
- 10. Analytics
- 11. Analytics Analytical DB Operational DB Operational DB Operational DB Business Activity Business Intelligence Updates, transactions Denormalized, Aggregations
- 12. Analytics OLAP OLTP OLTP OLTP Business Activity Business Intelligence ETL Schema knowledge Joins happen here
- 13. Analytics NoSQL DB OLTP OLTP OLTP Business Activity Business Intelligence ?
- 14. NoSQL and Analytics • Importing operational data can create a scale problem • Combining operational data can create sparse data • Operational schemas may change
- 15. NoSQL and Analytics Scalability, Schema Flexibility
- 16. Full Outer Join Cust.name Cust.age Orders.shoes Facebook.likes … Bob 43 $50 - … Sarah 32 $25 5/5/14 … Sally 28 - 4/3/12 … - - $35 11/1/13 … - - - 9/24/12 … Joe 45 $45 - … … … … … … Billions of rows Thousands of columns Sparse
- 17. BigTable Data Model Row ID Column Value R000 Cust.name Bob R000 Cust.age 43 R000 Orders.shoes $50 R002 Cust.name Sally R002 Cust.age 32 R002 Facebook.likes 4/3/12 … … …
- 18. MongoDB Data Model { ! ! Cust.name: “Bob”,! ! Cust.age: 43,! ! Orders.shoes: $50! },! {! ! Cust.name: “Sally”,! ! Cust.age: 32,! ! Facebook.likes: 4/3/12! },! …!
- 19. NoSQL Data Loading Shift NoSQL Analytics! ! Composite, Sparse Schemas Scale out Aggressive Indexing Data Discovery Conventional BI! ! Data cleaning Regularization Denormalization Star Schema Known operational Schemas
- 20. Analytics NoSQL DB OLTP OLTP OLTP Business Activity Business Intelligence Schema Discovery Joins happen here
- 21. NoSQL Analytics Shift Transformations! ! MapReduce Pre-computed Large answers Simple Lookups Queries! ! SQL Computed on the fly Small answers Roll up Drill down
- 22. Analytics NoSQL DB OLTP OLTP OLTP Business Activity Business Intelligence MapReduce Transformations Fast Lookups
- 23. MapReduce Analytics Supported! ! SQL (Hive) Statistical Modeling Machine Learning Text Analytics Feature Extraction Image Processing Graph Analysis
- 25. Combined-Data Security Requirements! ! Physically co-located data Strong logical access control Role-based
- 26. Questions ?