The document discusses four common problems encountered when building a DIY big data analytics system: 1) how to collect and store data, 2) how to query data, 3) how different users access query results, and 4) how to scale the system. It introduces Treasure Data as a solution that handles all these problems, allowing users to collect, store, query, access, and scale their data easily without having to manage infrastructure. Treasure Data provides analytics as a service using Hadoop and has tools that support data collection, querying, sharing results between different roles, and automatic scaling as more data and queries are added.

1. Four Problems You Run into When DIY-
ing a “Big Data” analytic system.
(and how to solve them. Hint: Treasure Data)
Kiyoto Tamura & Jeff Yuan

2. Before we begin…
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3. <announcements size=“two”>
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4. 1. we are hiring!
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5. 1. WE ARE HIRING!
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6. We are looking for…
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7. Lead UI/UX Designer
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8. 0
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9. which means…
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10. design the entire UI/UX
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11. 11

12. 12

13. 13

14. Anything that makes our
customer’s experience BETTER
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15. super important
high-responsibility
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16. Face of our service
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17. Lead UI/UX Designer
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18. careers@treasure-data.com
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19. We are also looking for…
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20. Engineers
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21. 21

22. (Hadoop) Engineers
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23. 23

24. 24

25. Distributed Systems
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26. specifically
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27. (multi-tenant) Hadoop
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28. Open Source!
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31. 31

32. class MemcacheList(object):
def push(self, key, value):
""" Add an element to the front of the list """
packed = msgpack.packb(value)
self.connection.append(key, packed)
def _unpack(self, data):
if data == 'x90':
return [], 0
_unpacker = msgpack.Unpacker()
_unpacker.feed(data)
32

33. class MemcacheList(object):
def push(self, key, value):
""" Add an element to the front of the list """
packed = msgpack.packb(value)
self.connection.append(key, packed)
def _unpack(self, data):
if data == 'x90':
return [], 0
_unpacker = msgpack.Unpacker()
_unpacker.feed(data)
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34. 34

35. (more on Fluentd later)
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36. #OneMoreThing
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37. 37

38. “way better than C++!”
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39. according to a committer
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40. (who works at Treasure Data)
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42. 42

43. www.treasure-data.com/careers/
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44. 1. We are hiring!
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45. 2. Discounts for Our Service!
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46. (ask us for the secret coupon code)
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47. 30% OFF
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48. 6 months
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49. 49

50. </announcements>
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51. Four Problems You Run into
When DIY-ing a “Big Data”
analytic system.
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52. 52

53. Hadoop as-a-Service!
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54. It’s a great idea
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55. more accessible and useful
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56. but also
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57. not so easy to implement
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58. e.g.
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59. 59

60. (zoom out)
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61. 61

62. Hadoop as-a-Service
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63. good in theory, lots of work in reality
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64. That’s where we come in!
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65. Easiest (and most cost effective) way
to get answers about my data!
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66.  Collect/Store
 Query
 Access
 Scale
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67. 1. How do I collect my data and how do I
store them?
 Stream (access logs, standard error)
 Bulk (historical data, sales
transactions, etc.)
 Secure and reliable storage!
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68. Client Server
Apache
App
App RDBMS
Other data sources
Treasure Data API
Layer
csv
json
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69. 2. How do I query my data?
 Ad hoc queries
 Scheduled queries
 Data schema?
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70. Cmdline,
console Query
API
HIVE, PIG (to be supported) Processing
Layer
Apps (JDBC, Cluster
User ODBC, REST)
MapReduce
Jobs
Amazon S3 Hadoop cluster
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71. 71

72. 3. How do different users in my org
access query results?
 Different roles need to access results
from different interfaces
• Analysts -> Excel
• Devs -> REST, MySQL
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73. Google Spreadsheet
ODBC -> Excel (Coming Q1)
Analysts
Treasure
Data MySQL, Postgres
JDBC, REST API
POST to web server Engineers
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74. 4. How do I scale?
 More data?
 More queries?
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75. Don’t worry, we’ll take care of it!
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76. Number of records in TD (in billions)
120
100
80
60
40
20
Sep Nov Jan Mar May Jul Aug
2011 2011 2012 2012 2012 2012 2012
January 2013 – Now over 200 Billion!
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77. Treasure Data High-Level Architecture
Log Data Spread Sheets
BI Tools
Application Data
Treasure Data
Subscribe
Data Warehouse SQL
td-agent Operational
3rd Party Data Interface Analytics
JDBC
ODBC
Databases
Sensor Data
Web/Mobile Data CLI
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78. Our Customers – Fortune Global 500
leaders and start-ups including:
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79.  Japan’s #1 recipe website
 15 million users
 1 million recipes
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80. MySQL to TD (Before)
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81. MySQL to TD (Before)
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82. MySQL to TD (After)
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83.  Europe’s largest independent
mobile ad exchange
 20 billion imps/month
 15,000+ mobile apps
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84. Two Weeks From Start to Finish!
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