1. Big Data Analytics from
a Practitioners view
Sep 2013
Raghu Kashyap

2. About Raghu Kashyap
page 1
Areas of Responsibility
 Data Insights Group (Site analytics,
Competitive Intelligence, Big Data)
 Orbitz India, supporting Analytics
and BI teams
 US, Europe, Australia(APAC)
Personal
 Director – Data Insights Group
 Strong background with technology(13
years) passion and experience with
analytics(4 years) and big data (3.5
year)
 Masters in Computer Science
 Golf, traveling, helping non-profit
organizations, spending time with my
wife and 2 boys
 Twitter: @ragskashyap
 Blog: http://kashyaps.com
 Email: raghu.kashyap@orbitz.com

3. Orbitz Worldwide
page 2

4. Challenges
 Lack of multi-dimensional capabilities
 Heavy investment on the tools
 Precision vs Accuracy
 Data Governance

5. continued….
 No data unification or uniform platform
across organizations and business
units
 No easy data extraction capabilities

6. Hadoop history at OWW
page 5

7. Web Analytics & Big Data
 OWW generates couple million air and hotel
searches every day.
 Massive amounts of data. Over hundred GB
of log data per day.
 Expensive and difficult to store and process
this data using existing data infrastructure.

8. Love Thy Hadoop
page 7
 Long term storage for
very large data sets.
 Open access to
developers and analysts.
 Allows for ad-hoc
querying of data and
rapid deployment of
reporting applications.

9. Hadoop Growth
page 8

10. Hadoop Cluster
page 9

11. Treemap of HDFS storage
page 10

12. Approach with Hadoop and ETL
Raw
logs
Flat files
Event Model
Map Reduce
ETL
External
Tables
Data Warehouse
(Greenplum)
GP Connector

13. Opportunities
page 12
Machine Learning
Site Analytics Data
PPC bidding efficiencies
Internal log analysis. Hgrep
MVT testing
Advanced Analytics

14. Show me the money
EFX – Every Friggin X
PPC bidding efficiencies
MAC vs. PC

15. Marketing Channel optimization
page 14
Orbitz.comDirect
Paid -
Brand
Paid –
Non
Brand
SEO –
Brand
SEO -
Non
Brand
Email
Meta
Travel
Research
Affiliates
Display
Ads

16. Hotel Rate Cache optimization
page 15
Data is collected as part of RCDC.
Includes every live rate search (aka
burst) performed by our hotel stack.
Raw data: ~200 GB, compressed, 108
records.
Extraction: <40 GB compressed, 109
records.

17. MVT
Analyze behavioral and Test data from our
MVT testing
page 16

18. DWH Log analysis
page 17
• Analysis of Greenplum DB logs within Hadoop
to analyze the data usage patterns.
• Impact analysis
• Hadoop usage for the last 30 days of DB log
analysis.

19. HIPPO is your best friend
• Expect organizational resistance from
unanticipated directions
• You can do wonders in the analytics area if
you get buy in.

20. Lessons Learnt
Analytics using Big Data comes with a price.
Data Governance
Senior Leadership buy in
I can't tell you the key to success, but the key
to failure is trying to please everyone." -Ed
Sheeran
page 19

21. How to capitalize on Big Data?
page 20
Learn from people who have already
done this.
DO NOT reinvent the wheel
Buy v/s Build balance
Build once and leverage mulitple
places.
Go where clients don’t want to go or
cant go in terms of execution.

22. What matters to Practitioners?
Things change dramatically in the
world of analytics
Being Agile is very important
Dashboards and Reports can take
you only to a certain level
Buy in from key groups is important
Grow business and impress Boss 
page 21

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Thank you