The presentation introduces Hadoop, Hive, and loggers. It discusses how data flows into Hadoop and the types of Hadoop including path optimization, basket analysis, next product to buy analysis, and granular customer segmentation. Examples of Hadoop include Intwritable, Long writable, Boolean writable, Float writable, and Byte writable. Hive is introduced as a data warehouse system for Hadoop that uses MapReduce for execution and HDFS for storage. The advantages and disadvantages of Hadoop are outlined along with applications such as marketing analytics, machine learning, image processing and web crawling.

1. Hadoop Presentation
Presenter : Ankitkumar
Varma

2. Topic
 Introduce to Hadoop
 Introduce to Hive
 Introduce to Logger
 Warehouse Mobion
 Advantages
 Disadvantages
 Applications
3/11/2016 Pham Thai Hoa

3. What is Hadoop
 Hadoop is a free, Java-based
programming framework that supports
the processing of large data sets in a
distributed computing environment. It
is part of the Apache project
sponsored by the Apache Software
Foundation.
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4. Data Flow into Hadoop
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5. Types of Hadoop
 Path Optimization – Path optimization
aims at reducing bounce rates and
improving conversions.
 Basket Analysis – This aims at
understanding aggregate customer
purchasing behavior by examining such
things as customer interests, and paths
to purchase – when customers bought
Product X, what common paths did they
take to get there.
3/11/2016 Pham Thai Hoa

6. Types of Hadoop
 Next Product to Buy Analysis – Related to
basket analysis, this type of analysis looks at
correlation in purchases, and what can be
offered next to help provide more immediate
value to the customer, and increase the
likelihood of another sale.
 Allocation of Website Resources – Having
clickstream data on hand, a company will
know what their hottest and coldest paths on
the site are and can assign development
resources accordingly, optimizing resource
allocation
3/11/2016 Pham Thai Hoa

7. Types of Hadoop
• Granular Customer Segmentation –
With clickstream and correlated user
data, a company can discover and
gain insight on how particular
segments and micro-segments of
customers are using the site, and how
to best cater to them.
3/11/2016 Pham Thai Hoa

8. Example of hadoop
 Intwritable
 Long writable
 Boolean writable
 Float writable
 Byte writable
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9. What is Hive
 Hive is a data warehouse system for
Hadoop
 Using Map-Reduce for execution
 Using HDFS for storage
 Metadata in an RDBMS
 Scalability and performance
 Interoperability
 Using a SQL-like language called
HiveQL
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10. Warehouse at Mobion
 Log Collector
 Log/Data Transformer
 Data Analyzer
 Web Reporter
 Log define
 Log integrate (into application)
 Log/Data analyze
 Report develop (API, Mobion, Music
…)
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11. Warehouse at Mobion
 Data mining
 Music Recommendation
 Spam Detection
 Application performance
 Export data and import into MySQL for
web report
 Analytic system
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12. Advantages
 Light weight persistence object
 High performance
 Scalability
 Error recovery:-it automatically
replicate the data its server or disk got
crashed.
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13. Performance
 Better reduce
 Impure data intigrity
 Impure security
 Application perforce is good
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14. Disadvantages
 Security is concerns
 Vulnerable by nature
 Not fit for small data
 Potential stability issues
 General limitation
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15. Applications
 Marketing analytics
 Machine learning or sophisticated data
mining
 Image processing
 Processing of XML messages
 Web crawling or text processing
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16. THANK YOU
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