Mike Miller is the Co-Founder and Chief Scientist of Cloudant, a company that provides a globally distributed data layer for web applications. He has a background in machine learning, analysis, big data, and distributed systems. Cloudant was founded in 2009 by MIT data scientists and provides a hyper-scalable document database and analytics platform that runs across multiple data centers.

1. Mike Miller, CoFounder, Chief Scientist
@mlmilleratmit
mike@cloudant.com 1

2. My Background
Cloudant CoFounder, Chief Scientist
Assistant Professor, Particle Physics
(U. Washington, Affiliate)
Background: machine learning,
analysis, big data, globally distributed
systems
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3. The face of big data
http://abstract.cs.washington.edu/~shwetak/
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4. The face of big data
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5. The face of big data
“The future is stranger and sooner than you think”
Reid Hoffman, LinkedIn/Greylock
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6. Perfect Storm
Parallel
Processing
Big Data
HTML5/JS
Mobile 9M Trained
Developers
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7. Focus on your Application
not data operations
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8. If your data is stuck in the warehouse...
... you’re losing
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9. Data Layer for the Web
Founded (2009) by leading MIT data
scientists
Funded by Y Combinator & Avalon
Global network of 20+ data centers
-- Application Data Network (ADN)
Built on leading NoSQL standard:
most durable data store on planet
10,000 users and growing.
Cloudant: Akamai of dynamic content
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10. Cloudant Product Line
• Application State
Hyper-Scalable Document Store (JSON+HTTP)
MVCC
Secondary indexes for flexible query
• Application Data Security
Accounts/API keys, data sharing, permission roles
• Application Analytics
Fully Integrated (Incremental) MapReduce engine
• Application Search
Fully Integrated (Incremental) Lucene + Geospatial
API Compatible
• Application Object Storage
images, audio, video...
• Application State Distribution
cloud <==> tablet <==> PC <==> mobile
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11. Cloudant Install
You do this:
We give you:
That’s It
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12. API Examples
Write a doc...from the browser
No client install necessary
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13. API Examples
Create Secondary Indexes
Query Those indexes
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14. http://examples.cloudant.com/lobby-search/_design/lookup/index.html
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15. Global Data Network
Cloudant scales within & between data centers
Availability, low-latency
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16. Anatomy of the Data Layer
PUT {document} Secondary Data Centers
(for DR & distributed access)
US-EAST “Node”
AP-JP
Filtered Replication &
Sync EU-NL
Single-tenant
cluster
Multi-tenant
cluster
Disconnected
Horizontally Scalable DB
Devices
• Fault tolerant
• Always consistent Edge Database
• Schemaless (NoSQL) Cluster
• Automatic sharding
• Distributed, parallel analytics
• Incremental, chainable
MapReduce
• Full-text search Single-Tenant or Multi-Tenant
16

17. https://cloudant.com/blog/cloudant-labs-on-google-spanner/
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18. Why It Matters
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19. >1. Visualization Wins
http://sosolimited.com/blog/2012/07/from-tweets-to-lightshow/
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20. >2. Prepare For Success
Three #1 apps, from 6 to 90 servers in weeks
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21. >3. Scale Invariance
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22. >3. Scale Invariance
mobile/tablet
desktop
Goal: Megabytes to Petabytes Cloud
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23. >3. Scale Invariance
‘Carry Small, Live Large’
single user experience at vastly different scales
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24. >4. No Preferred Frame
So why do you have a global ‘write master’?
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25. >4. No Preferred Frame
This simple document...
...establishes Continuous Pipe from Europe to US
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26. >4. No Preferred Frame
And you can do the reverse...
...at the same time
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27. >4. No Preferred Frame
Write local, live global
What could you do with relaxed constraints?
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28. >4. No Preferred Frame
Data Import
18 18
Size [GB]
Doc Count [Million]
Actual Customer Data
France to Amsterdam
16 Data Size [GB] 16
Disk Size [GB]
14 14
Documents [M]
12 12
10 10
8 8
6 6
4 4
2 2
0 0
0 2000 4000 6000 8000 10000 12000 14000
Time [sec]
One click (continuous) Import
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29. Big and Getting Bigger
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30. Big and Getting Bigger
• And of course, we are hiring
Languages
erlang, scala, c, javascript, python, clojure, html5, iOS, Android, ruby/chef
Sample problems in the Seattle office
Create file format optimized for (huge) structured time-series data
Integrate Cubism into two-tier application stack
Profile creation of 100M databases (real customer)
PIG / HIVE integration
Prototype read-in-place Hadoop connector
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