This document provides an agenda for a workshop on Hadoop and Spark. It begins with background on big data, analytics, and data science. It then outlines workshops that will be conducted on installing and using Hadoop and Spark for tasks like word counting. Real-world use cases for Hadoop are also discussed. The document concludes by discussing trends in Hadoop and Spark.

1. Hadoop for (Young) Data Scientist
Komes Chandavimol and Team
Data Science Lab, Thailand
komes@datascienceth.com

2. Agenda
• Big Data, Analytics and Data Science
• Hadoop + Sparks Workshops
• Sharing Experience: Hadoop (Real) Use Cases
• Hadoop + Spark Trends,

3. 3
Big Data, Analytics and Data Science

4. Big Data
http://www.adweek.com/prnewser/how-many-times-do-the-worlds-social-media-users-click-every-minute/117427
https://www.domo.com/learn/data-never-sleeps-3-0

5. Internet of Things
http://topmanagement.com.mx/innovacion-social-y-empresarial-objetivo-de-hitachi/

6. 6
http://www.adweek.com/prnewser/how-many-times-do-the-worlds-social-media-users-click-every-minute/117427
https://www.domo.com/learn/data-never-sleeps-3-0
The Growth of Data

7. 7
http://www.adweek.com/prnewser/how-many-times-do-the-worlds-social-media-users-click-every-minute/117427
https://www.domo.com/learn/data-never-sleeps-3-0
What is Big Data?

8. 8
http://blogs.forrester.com/category/hadoop
http://solutions.forrester.com/Global/FileLib/webinars/Big_Data_-_Gold_Rush_or_Illusion.pdf
The Big Data Tools

9. http://thebigdatablog.weebly.com/blog/the-hadoop-ecosystem-overview

11. 11
http://hortonworks.com/blog/optimize-your-data-architecture-with-hadoop/
Traditional Data Management Architecture

12. 12
http://hortonworks.com/blog/optimize-your-data-architecture-with-hadoop/
New Data Management Architecture

13. 13
http://www.kdnuggets.com/2014/05/big-data-landscape-v30-analyzed.html

14. 14
https://www.digitalnewsasia.com/business/forget-data-warehousing-its-data-lakes-now
Data Lake

15. How the Data Lake works?
15
http://www.clearpeaks.com/blog/category/tableau
Traditional Enterprise Data warehouse

16. 16
What you consume from Data Lake?
https://www.digitalnewsasia.com/business/forget-data-warehousing-its-data-lakes-now

17. 17
Volume? Variety? Velocity?
https://www.digitalnewsasia.com/business/forget-data-warehousing-its-data-lakes-now

18. 18
Value
https://www.digitalnewsasia.com/business/forget-data-warehousing-its-data-lakes-now

19. 19
Big Data + Analytics = Values
https://www.digitalnewsasia.com/business/forget-data-warehousing-its-data-lakes-now

20. Big Data Analytics
20http://hortonworks.com/blog/big-data-refinery-fuels-next-generation-data-architecture/

21. Big Data Analytics
21
http://dataofthings.blogspot.com/2014/04/the-bbbt-sessions-hortonworks-big-data.html

22. Big Data Analytics
22http://www.gartner.com/it-glossary/predictive-analytics

23. 23
How to do Big Data Analytics?
https://www.digitalnewsasia.com/business/forget-data-warehousing-its-data-lakes-now

25. Data Science Experience Sharing, Big Data Challenge #2,Bangkok Thailand
http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
What is Data Science?

26. The Rise of Data Scientist
27
http://flowingdata.com/2009/06/04/rise-of-the-data-scientist/
2009
https://hbr.org/

27. 28http://hrb.org
http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html
2014
The Rise of Data Scientist

28. Data Science Experience Sharing, Big Data Challenge #2,Bangkok Thailand
http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html
2014
The Data Science

29. 30
The Solution, Data Science Team

30. 31
Data Science Team
Doing Data Science by O'Neil et al (2013)

31. 32
Doing Data Science by O'Neil et al (2013)

32. 33
Doing Data Science by O'Neil et al (2013)
Data Science Team
Analyzing the Analyzers, Harris (2013)

33. 34
Data Science Team
Data Scientist & Data Engineer
http://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html

34. 35
Data Science Team
Data Scientist & Data Engineer
http://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html
https://www.facebook.com/DataScienceTh/posts/931828353527079:0

35. 36
Data Science Professionals
http://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html

36. 37
Data Science for Dummies Pierson
(2015)
∗Build In-house Team
• Train existing employee
• Train existing employee and hire experts
• Hire experts
∗Outsourcing requirements to private DS consultants
• Outsourcing for comprehensive DS Strategy development
• Outsource for DS Solutions to specific problem
∗Leverage Cloud-based platform solutions
How to build DS Team?

37. Machine Learning
Improving Performance in some Task with Experience”. Tom Mitchell
Tom Mitchell (1998)
The field of study that gives computers the ability to learn
without being explicitly programmed. Arthur Samuel (1990)
Wikipedia, Data Visualization for Dummies (2014)
Data Points: Visualization That Means Something(2013)
38
Machine Learning deals with systems
that can learn from data.

38. 39

39. Machine Learning  Discovery
• Class Discovery
• Correlation Discovery
• Novelty (Surprise) Discovery
• Association (or Link Discovery)
40
KirkBorne-workshop-ODSC2016.pdf

40. The XYZ of Data Science
Smart X :
• Smart Cities
• Smart Highways
• Smart Supply Chain
Precision Y :
• Precision Medicine
• Precision Farming
• Precision Pricing
Personalized Z :
• Personalized Health
• Personalized Learning
• Personalized Shopping Experience
41
KirkBorne-Workshop-ODSC2016.pdf
Intelligence at the edge of the network… at the point of data collection

41. 42DataInquest – Predictive Analytics and Data Science Bootcamp

42. Data Science is a Team Sport
http://www.ibmbigdatahub.com/blog/why-data-science-team-sport

43. 44
How to Start?

44. 45
Hadoop + Spark Workshops

48. 49
Workshop #1 การติดตั้ง HDFS และ YARN

50. 51
Workshop #2 WordCount

52. 53
Workshop #3 WordCount (Streaming)

53. 54
Workshop #4 WordCount
(Frequency Sort)

55. 56
Workshop #5 Setup Cloudera
QuickStart

57. 58
Workshop #6
Exploring HBASE data in HUE

58. 59
Workshop #7
Design a Schema for quick twitter
relationship lookup

59. 60
Workshop #8
Design a schema for IoT log
(Smart Meter)

60. 61
Workshop #9
Create an HBase table for
Smart meter data

61. 62
Workshop #10
Bank Customer Snapshot

64. 65
Workshop #10.1 -
10.1 Create Hive Tables
10.2 Create External Hive Tables
10.3 Create External Hive Tables
10.4 Partition

66. 67
Workshop #11
SQOOP

72. 73
Workshop
spk1 WordCount
spk2 WordCount
spk3 WordCount

75. 76
Workshop
spk4 SparkSQL + ML

83. 84
Sharing Experience:

84. Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute for Business Value study.
Copyright © Massachusetts Institute of Technology 2010.
Top Performers Use Analytics 5
Times More Than Lower
Performers

85. Revenue - Cost = Profit

86. Monitoring and Maintenance
Data sources: IoT Sensors in factory
Data products: predictive maintenance models
http://www.electrex.it/en/news/600-automated-energy-management-system-a-enms-for-cement-production-plants.ht

87. Customer Engagement + Location
Data sources: Mobile App, Loyalty Program, GIS
Data products: Buying behavior analysis, coupon-response model , location visualization
http://www.fastcompany.com/3020859/most-creative-people/how-chinas-one-child-policy-forced-starbucks-to-rethink-its-beijing-sto

88. Fuel Saving
Data sources: Telematics (sensor), GPS
Data products: Prescriptive analytics – route
optimization, predictive maintenance
(parts/malfunction)
http://www.cnet.com/news/ups-turns-data-analysis-into-big-savings/

89. Fraud Detection
Data sources: historical pattern of transaction data
Data products: predictive models – fraud/non-fraudhttps://bluefishway.com/2013/09/13/panic-oh-no-not-again/

90. HR Analytics – Google Hiring
Data sources: Historical hiring attributes
Data products: Predictive model – recruiting
high performer
Behavioral Test
Situational Test
GPA
Brain Teaser
Good School

91. Average ROI of Analytics/Data Science

92. 93
Hadoop + Spark Trends