This webinar will first walk through the main forces driving developers and IT organizations to adopt non-relational or NoSQL databases. Next it will cover the key concepts and terminology used in the NoSQL space. Finally, using MongoDB as an example, the webinar will highlight examples of how organizations have put NoSQL technology to use in order to drive their business objectives.

1. An Introduction to NoSQL:
Theory and Practice
Nosh Petigara
nosh@10gen.com
@noshinosh

2. Today
• Why new databases?
• What is „NoSQL‟?
• A brief introduction to MongoDB
• Some real-world use cases

3. Database Evolution
RDBMS
Oracle,
MySQL,
PostgreSQL
OLAP NoSQL
Netezza, MongoDB,
Vertica, CouchDB,
Hadoop Cassandra

4. Why NoSQL?
• explosion of data and our desire to make
Big Data meaningful decisions from that data
New
• the existing data model is an impediment to
Programming agile development.
models
New Hardware • The Cloud is starting to become the dominant
deployment architecture. Databases need to
Architecture take advantage of horizontal scaling capacity

5. Trends

6. What should my database be like?
• Enable faster development cycle
Agile • Deal with structured and unstructured
data
• Billions of objects, high read/write
Scalable volume, terabytes/petabytes
• Cost effectively operationalize data in
Cloud-ready cloud-like environments

7. The Great Divide
Sweet spot: Agile, Flexible, Scalable

8. How do I evaluate a NoSQL/BigData
Solution?
• Real-time vs. Batch
• Data Model
• Distribution Model + Consistency

9. Some comparisons
• Realtime vs. Batch
• Realtime: MongoDB, Cassandra, Membase, RDBMS
• Batch: Hadoop, traditional data warehousing/BI
• Data models
• Relational: Oracle, MySQL, etc
• Key-value: Membase, Redis
• Document: MongoDB, CouchDB
• Column/Tabular: Cassandra
• Distribution & consistency
• Eventual consistency: Cassandra, Dynamo (S3, SimpleDB), RIak
• Regular consistency: MongoDB, Oracle, etc

10. Some commonalities
• No Joins
• Relaxed transactional semantics
• No joins + simple transactions -> easier
horizontal scalability

11. What to look for
• Can I model my data
• Can I query my data
• Can I update my data
• Does it support my operational needs

12. NoSQL in Practice: MongoDB
• Open source
• Non-relational, document-oriented
• Dynamic Schemas
• Regular consistency: Scale-out by auto-
sharding (Similar to Google File System)

13. Data as Documents: A blog post
Primary key
{
_id:“A4304”
Simple values
author: “nosh”,
date: 22/6/2010,
title: “Intro to MongoDB” Arrays
text: “MongoDB is an open source..”,
tags: [“webinar”, “opensource”]
comments: [{
author: “mike”,
date: 11/18/2010,
txt: “Did you see the…”,
votes: 7
},….]
}
Embedded documents

14. MongoDB is:
Application
Document
Oriented
{ author: “roger”,
date: new Date(),
text: “Spirited Away”,
tags: [“Tezuka”, “Manga”]}
Horizontally Scalable

15. Photo Meta-Data
Problem:
• Store metadata for billions of photos and videos
• Business needed more flexibility than Oracle could deliver
Solution:
• Used MongoDB instead of Oracle
Results:
• Developed application in one sprint cycle
• 500% cost reduction compared to Oracle
• 900% performance improvement compared to Oracle
http://10gen.com/customers

16. Real-time Customer
Analytics
Problem:
• Track customer activity in real-time across huge
• Deal with massive data volume across all customer sites
Solution:
• Used MongoDB to replace Google Analytics / Omniture options
Results:
• Less than1week to build prototype and prove business case
• Rapid deployment of new features (1/day, 1/week)
http://10gen.com/customers

17. Data Archiving
Problem:
• Archive years of postings for compliance
• RDBMS could not handle evolving schemas
Solution:
• Used MongoDB to replace MySQL
Results:
• Less than1week to build prototype and prove business case
• Rapid deployment of new features (1/day, 1/week)
http://10gen.com/customers

18. Next Steps
• nosh@10gen.com
• @noshinosh
• http://mongodb.org
• http://10gen.com/presentations
• http://10gen.com/jobs

19. Next Sp

20. • Easy to start
• Easy to develop
• Easy to scale