Treasure Data is a cloud-based big data analytics company based in Silicon Valley with about 20 employees. The document discusses Treasure Data's services and architecture, which includes collecting data from various sources using Fluentd, storing the data in a columnar format on AWS S3, and performing analytics using Hadoop and SQL queries. Treasure Data aims to simplify big data adoption through its fully-managed platform and quick setup process. Example customers discussed were able to see results within 2 weeks of signing up.

1. Treasure Data Cloud Strategy
Masahiro Nakagawa
July Tech Festa: Jul 14, 2013
Sunday, July 14, 13

2. Who are you?
§ Masahiro Nakagawa
• @repeatedly / masa@treasure-data.com
§ Treasure Data, Inc.
• Senior Software Engineer, since 2012/11
§ Open Source projects
• D Programming Language
• MessagePack: D, Python, etc...
• Fluentd: Core, Mongo, Logger, etc...
• etc...
2
Sunday, July 14, 13

3. Treasure Data overview
Sunday, July 14, 13

4. Company Overview
§ Silicon Valley-based Company
• All Founders are Japanese
• Hironobu Yoshikawa
• Kazuki Ohta
• Sadayuki Furuhashi
• About 20 people
• Over 3.5 million jobs
§ OSS Enthusiasts
• MessagePack, Fluentd, etc.
4
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5. Investors
§ Bill Tai
§ Othman Laraki - Former VP Growth at Twitter
§ James Lindenbaum, Adam Wiggins, Orion Henry -
Heroku Founders
§ Anand Babu Periasamy, Hitesh Chellani - Gluster
Founders
§ Yukihiro “Matz” Matsumoto - Creator of Ruby
§ Dan Scheinman - Director of Arista Networks
§ Jerry Yang - Founder of Yahoo!
5
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6. 6
DataVolume
Cloud
Enterprise
RDBMSLightweight
RDBMS
DB2
1Bil entry
Or 10TB
Traditional
Data Warehouse
$10B
market
$34B
market
Database-as-a-service
Big Data-as-a-Service
On-Premise
© 2012 Forrester Research, Inc. Reproduction Prohibited
Treasure Data = Cloud + Big Data
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7. The Problem with Other Solutions
7
Customer
Value
Time
Sign-up or PO
On-Premise
Solutions
Obsolescence
over time
Treasure Data
Fully integrated Big Data full-
stack service with simple
interface, low friction initial
engagement & continuous
technical upgrade
Need Upgrade
AWS
(or hosted Hadoops)EC2
EMR
RedShift
S3 Step-by-step manual
integrations
Maintain
NO SpecialistsTOO LONG to get Live
=
Complex Solutions
+
Data Collection
+
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8. 8
Big Data Adoption Stages
Intelligence Sophistication
Standard Reports
Ad-hoc Reports
Drill Down Query
Alerts
Statistical Analysis
Predictive Analysis
Optimization
What happened?
Where?
Where exactly?
Error?
Why?
What’s a trend?
What’s the best?
Analytics
Reporting
Sunday, July 14, 13

9. 8
Big Data Adoption Stages
Intelligence Sophistication
Standard Reports
Ad-hoc Reports
Drill Down Query
Alerts
Statistical Analysis
Predictive Analysis
Optimization
What happened?
Where?
Where exactly?
Error?
Why?
What’s a trend?
What’s the best?
Analytics
Reporting
Treasure Data’s FOCUS
(80% of needs)
Sunday, July 14, 13

10. 9
Full Stack Support for Big Data Reporting
Our best-in-class architecture
and operations team ensure the
integrity and availability of your
data.
Data from almost any source
can be securely and reliably
uploaded using td-agent in
streaming or batch mode.
Our SQL, REST, JDBC, ODBC
and command-line interfaces
support all major query tools
and approaches.
You can store gigabytes to
petabytes of data efficiently and
securely in our cloud-based
columnar datastore.
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11. We are...
10
Big Data as a Service
not
Hadoop on Cloud
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12. Columnar Storage
+
Hadoop
MapReduce
600 bil+ records
3.5 mil+ jobs
Product
11
Data Collection Data Warehouse Data Analysis
Open-Source
Log Collector
2,500+ companies
(incl. LinkedIn, etc)
Bulk Loader
CSV / TSV
MySQL,
Postgres
Oracle, etc.
Web Log
App Log
Sensor
RDBMS
CRM
ERP
Streaming Upload
60billion / month
BI Tools
Tableau, QlickView,
Pentaho, Excel, etc.
REST
JDBC / ODBC
SQL
(HiveQL)
or
Pig
Bulk Upload
Parallel Upload
Value Proposition:
“Time-to-Answer” 20bil+, 2 weeks,
UK/Austria
3bil+, 3 weeks
Singapore
2 weeks,
US
2 weeks,
US
3 weeks,
Japan
Dashboard
Custom App,
RDBMS, FTP, etc.
Result push
Multi-Tenant: Single Code for Everyone - Improving the Platform Faster (e.g. SFDC, Heroku)
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13. 12
Our Customers – 80 companies
http://docs.treasure-data.com/categories/success-stories
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14. 13
A case: “14 Days” from Signup to Success
1. Europe’s largest mobile ad
exchange.
2. Serving >20 billion imps/
month for >15,000 mobile
apps (Q1 2013)
3. Immediate need of analytics
infrastructure: ASAP!
4. With TD, MobFox got into
production only in 14 days,
by one engineer.
"Time is the most precious asset in our fast-moving business,
and Treasure Data saved us a lot of it."
Julian Zehetmayr, CEO & Founder
td-agent =
fluentd rpm/deb
Sunday, July 14, 13

15. 14
A case: “Replace” in-house Hadoop to TD
1. Global “Hulu” - Online Video
Service with millions of users
2. Video contents are distributed
to over 150 languages.
3. Had hard time maintaining
Hadoop cluster
4. With TD, Viki deprecated their
in-house Hadoop cluster and
use engineer for core
businesses.
Before
After
“Treasure Data has always given us thorough and timely support
peppered with insightful tips to make the best use of their service."
Huy Nguyen, Software Engineer
Sunday, July 14, 13

16. 15
A case: Treasure Data with BI Tool (Tableau)
1. World’s largest android
application market
2. Serving >3 billion app
downloads for >100 million
users
3. Only one engineer managing
the data infrastructure
4. With TD, the data engineer can
focus on analyzing data with
existing BI tool
"I will recommend Treasure Data to my friends in a heartbeat because it
benefits all three stakeholders: Operations, Engineering and Business."
Simon Dong, Principal Architect - Data Engineering
Sunday, July 14, 13

17. 16
- Vision -
Single Analytics Platform for the World
http://www.chisite.org/initiatives/WGII
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18. Treasure Data’s
Service Architecture
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19. 18
Treasure Data = Collect + Store + Query
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20. 19
Architecture Breakdown
Data Collection
• Increasing variety of
data sources
• No single data schema
• Lack of streaming data
collection method
• 60% of Big Data project
resource consumed
Data Store/Analytics
• Remaining complexity in
both traditional DWH
and Hadoop (very slow
time to market)
• Challenges in scaling
data volume and
expanding cost.
Connectivity
• Required to ensure
connectivity with
existing BI/visualization/
apps by JDBC, ODBC
and REST.
• Output ot other services,
e.g. S3, RDBMS, etc.
Sunday, July 14, 13

21. Product Philosophy
§ Data first, Schema later
• “Schema-on-Read”
• Both Batch and Query processing
§ Simple APIs
• Easy to use and powerful
§ Easy integration
• Log collecting, BI tools and etc...
20
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22. Our technology stack
§ td-agent
• ETL part of Treasure Data
§ Plazma
• Big data processing infrastructure
• Columnar oriented storage
• Reliable data handling
§ Multi-tenant scheduler
• Robust distributed queue and scheduler
21
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23. § 60% of BI project resource is consumed here
§ Most ‘underestimated’ and ‘unsexy’ but MOST important
§ Fluentd: OSS lightweight but robust Log Collector
• http://fluentd.org/
1) Data Collection
22
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24. Apache
App
App
Other data sources
td-agent
RDBMS
Treasure Data
columnar data
warehouse
Query
Processing
Cluster
Query
API
HIVE, PIG
JDBC, REST
User
td-command
BI apps
23
This!
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25. fluentd.org
Fluentd
the missing log collector
24
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26. Data Processing
Collect Store Process Visualize
Data source
Reporting
Monitoring
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27. Store Process
Cloudera
Horton Works
Treasure Data
Collect Visualize
Tableau
Excel
R
easier & shorter time
???
Related Products
Sunday, July 14, 13

28. In short
§ Open sourced log collector written in Ruby
• Easy to use, reliable and well performance
• like streaming event processing
§ Using rubygems ecosystem for plugins
27
It’s like syslogd, but
uses JSON for log messages
Sunday, July 14, 13

29. tail
insert
event
buffering
127.0.0.1 - - [11/Dec/2012:07:26:27] "GET / ...
127.0.0.1 - - [11/Dec/2012:07:26:30] "GET / ...
127.0.0.1 - - [11/Dec/2012:07:26:32] "GET / ...
127.0.0.1 - - [11/Dec/2012:07:26:40] "GET / ...
127.0.0.1 - - [11/Dec/2012:07:27:01] "GET / ...
...
28
Fluentd
Web Server
Example (apache to monogdb)
2012-12-11 07:26:27
apache.log
{
"host": "127.0.0.1",
"method": "GET",
...
}
Sunday, July 14, 13

30. Application
･･･
Server2
Application
･･･
Server3
Application
･･･
Server1
FluentLog Server
High Latency!
must wait for a day...
29
Before Fluentd
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31. Application
･･･
Server2
Application
･･･
Server3
Application
･･･
Server1
Fluentd Fluentd Fluentd
Fluentd Fluentd
In streaming!
30
After Fluentd
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32. Buffer Output
Input
> Forward
> HTTP
> File tail
> dstat
> ...
> Forward
> File
> MongoDB
> ...
> File
> Memory
31
Pluggable architecture
Engine
Output
> rewrite
> ...
Pluggable Pluggable
Sunday, July 14, 13

33. Nagios
MongoDB
Hadoop
Alerting
Amazon S3
Analysis
Archiving
MySQL
Apache
Frontend
Access logs
syslogd
App logs
System logs
Backend
Databases
buffer / filter / routing
32
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34. td-agent
§ Open sourced distribution package of Fluentd
• ETL part of Treasure Data
• rpm, deb and homebrew
§ Including useful components
• ruby, jemalloc, fluentd
• 3rd party gems: td, mongo, webhdfs, etc...
• td plugin is for Treasure Data
§ http://packages.treasure-data.com/
33
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35. § Remaining complexity in both DWH and Hadoop
§ Challenges in scaling data volume and expanding cost
§ Plazma: Hadoop eco system and own projects
2) Data Store / Analytics
34
Sunday, July 14, 13

36. Apache
App
App
Other data sources
td-agent
RDBMS
Treasure Data
columnar data
warehouse
Query
Processing
Cluster
Query
API
HIVE, PIG
JDBC, REST
User
td-command
BI apps
35
This!
Sunday, July 14, 13

37. AWS Component Dependencies (1)
§ RDS
• Store user information, job status, etc...
• Store metadata of our columnar database
• Queue worker / Scheduler
§ EC2
• API servers (Ruby on Rails 3)
• Hadoop clusters
• Job workers
• Using Chef to deploy
36
Sunday, July 14, 13

38. AWS Component Dependencies (2)
§ ELB
• Load balancing of API servers
• Load balancing of td-agents
§ S3
• Columnar storage built on top of S3
• MessagePack columnar format
• Realtime / Archive storage
• Our Result feature supports S3 output.
37
No EBS, EMR, SQS and other products !
Sunday, July 14, 13

39. Frontend
Queue
Worker
Hadoop
Fluentd
Applications push
metrics to Fluentd
(via local Fluentd)
Librato Metrics
for realtime analysis
Treasure
Data
for historical analysis
Fluentd sums up data minutes
(partial aggregation)
Treasure Data Service Processing Flow
38
Hadoop
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40. 39
Data Processing Flow
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41. Structure of Columnar Storages
Realtime Storage
merge (every 1 hour)
2013-07-12 00:23:00 912ec80
2013-07-13 00:01:00 277a259
2013-07-14 00:02:00 d52c831
...
23c82b0ba3405d4c15aa85d2190e
6d7b1482412ab14f0332b8aee119
8a7bc848b2791b8fd603c719e54f
0e3d402b17638477c9a7977e7dab
...
SELECT ...
Archive Storage
Data import
40
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42. Query Language
Query Execution
Columnar Data
Object Storage
41
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43. 1/4: Compile SQL into MapReduce
SELECT COUNT(DISTINCT ip) FROM tbl;
SQL Statement
Hive
SQL - to - MapReduce
42
+TD UDFs
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44. 2/4: MapReduce is executed in parallel
SELECT COUNT(DISTINCT ip) FROM tbl;
43
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45. 3/4: Columnar Data Access
Read ONLY the Required Part of Data
SELECT COUNT(DISTINCT ip) FROM tbl;
44
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46. 4/4: Object-based Storage
45
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47. Apply Schema
{“user”:54, “name”:”test”, “value”:”120”, “host”:”local”}
Schema user:int name:string value:int
SELECT 54 (int)
Raw data（JSON）
“test” (string) 120 (int)
host:int
NULL
46
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48. Multi-Tenancy
§ All customers share the Hadoop clusters (Multi Data Centers)
§ Resource Sharing (Burst Cores), Rapid Improvement, Ease of Upgrade
47
datacenter A
datacenter B
datacenter C
datacenter D
Local FairScheduler
Local FairScheduler
Local FairScheduler
Local FairScheduler
Global
Scheduler
On-Demand
Resouce Allocation
Job Submission
+ Plan Change
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49. Trial and error on Cloud
§ Rapid development
• Change hardware
• New architecture testing
• Performance testing
• Change software
• Hadoop parameters
• etc...
§ Use git and chef for these purposes
• Easy to deploy and apply changes
• git for change history
48
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50. § Services
• CopperEgg
• Librato Metrics
• Logentries
• NewRelic
• PagerDuty
• Desk.com
• Olark
• HipChat
• Alerting
Our Operation Stack: Full Use of SaaS
49
§ Tools
• Hosted Chef (Opscode)
• Jenkins
• including integration test
44
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51. Sunday, July 14, 13

52. Sunday, July 14, 13

53. Sunday, July 14, 13

54. 53
3) Connectivity
§ Need to visualize the query result
§ Use metrics / graph for interactive comparison
§ Result: Export result and use existence tools
45
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55. Apache
App
App
Other data sources
td-agent
RDBMS
Treasure Data
columnar data
warehouse
Query
Processing
Cluster
Query
API
HIVE, PIG
JDBC, REST
User
td-command
BI apps
54
This!
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56. 55
Pull and Push approaches
Query
(Pull)
Web App
MySQL
Treasure Data
Columnar Storage
Query
Processing
Cluster
Query
API
REST API
JDBC, ODBC Driver
td-command
BI apps
S3
Result
(Push)
…
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57. Support list
56
§ Result
• Treasure Data
• MySQL
• PostgreSQL
• Google SpreadSheet
• REST API
• S3
• etc...
§ BI tool
• Pentaho
• Tableau
• JasperSoft
• Indicee
• Dr. Sum
• Metric Insight
• etc...
http://docs.treasure-data.com/categories/3rd-party-tools-overview
http://docs.treasure-data.com/categories/result
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58. § Treasure Data
• Cloud based Big-data analytics platform
• Provide Machete for Big data reporting
§ Big Data processing
• Collect / Store / Analytics / Visualization
§ Consider trade-off
• Cloud reinforces idea but not differentiator
• What is the strong point?
• Should focus own vision!
Conclusion
57
Our focus!
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59. Big Data for the Rest of Us
www.treasure-data.com | @TreasureData
Sunday, July 14, 13