10 Common Hadoop-able Problems Webinar

10 Common Hadoop-able
Problems
August 5, 2010

Topics

• Introduction
• 10 Common Hadoop-able Problems
• Summary
• Questions

Copyright 2010 Cloudera Inc. All rights reserved 2

Today’s speaker - Jeff Hammerbacher

• hammer@cloudera.com
• Studied Mathematics at Harvard
• Worked as a Quant on Wall Street
• Conceived, built, and led Data team at Facebook
• Nearly 30 amazing engineers and data scientists
• Several open source projects and research papers
• Founder of Cloudera
• Chief Scientist
• Also, check out the book “Beautiful Data”


What is Hadoop?

• A scalable fault-tolerant distributed system for data storage
and processing (open source under the Apache license)

• Scalable data processing engine
• Hadoop Distributed File System (HDFS): self-healing high-bandwidth
clustered storage
• MapReduce: fault-tolerant distributed processing

• Key value
• Flexible -> store data without a schema and add it later as needed
• Affordable -> cost / TB at a fraction of traditional options
• Broadly adopted -> a large and active ecosystem
• Proven at scale -> dozens of petabyte + implementations in
production today
Copyright 2010 Cloudera Inc. All Rights Reserved. 4

Cloudera’s Distribution for Hadoop, version 3
The industry’s leading Hadoop distribution

Hue Hue SDK

Oozie Oozie Hive
Pig/
Hive

Flume, Sqoop HBase

Zookeeper

• Open source – 100% Apache licensed
• Simplified – Component versions & dependencies managed for you
• Integrated – All components & functions interoperate through standard API’s
• Reliable – Patched with fixes from future releases to improve stability
• Supported – Employs project founders and committers for >70% of components

How does Cloudera know which problems are
Hadoop-able?

• Talking to 1000s of users
• Supporting 100s of implementations
• Experience putting Hadoop into production with
customers across a range of industries


Summary – 10 Common Hadoop-able Problems

1. Modeling true risk 6. Analyzing network data
to predict failure
2. Customer churn
analysis 7. Threat analysis
3. Recommendation 8. Trade surveillance
engine
9. Search quality
4. Ad targeting
10. Data “sandbox”
5. PoS transaction analysis


What is common across Hadoop-able problems?

Nature of the data
• Complex data
• Multiple data sources
• Lots of it

Nature of the analysis
• Batch processing
• Parallel execution
• Spread data over a cluster of servers
and take the computation to the data


What Analysis is Possible With Hadoop?

• Text mining • Collaborative filtering
• Index building • Prediction models
• Graph creation and • Sentiment analysis
analysis
• Risk assessment
• Pattern recognition


Benefits of Analyzing With Hadoop

• Previously impossible/impractical to do this analysis

• Analysis conducted at lower cost

• Analysis conducted in less time

• Greater flexibility


Topics

• Introduction
• Summary
• Questions


1. Modeling True Risk


1. Modeling True Risk
Solution with Hadoop
• Source, parse and aggregate disparate data
sources to build comprehensive data picture
• e.g. credit card records, call recordings, chat
sessions, emails, banking activity
• Structure and analyze
• Sentiment analysis, graph creation, pattern
recognition

Typical Industry
• Financial Services (Banks, Insurance)

2. Customer Churn Analysis


2. Customer Churn Analysis
• Rapidly test and build behavioral model of customer
from disparate data sources
• Structure and analyze with Hadoop
• Traversing
• Graph creation

Typical Industry
• Telecommunications, Financial Services

3. Recommendation Engine


3. Recommendation Engine

• Batch processing framework
• Allow execution in in parallel over large datasets
• Collaborative filtering
• Collecting ‘taste’ information from many users
• Utilizing information to predict what similar
users like

Typical Industry
• Ecommerce, Manufacturing, Retail

4. Ad Targeting


4. Ad Targeting

• Data analysis can be conducted in parallel, reducing
processing times from days to hours
• With Hadoop, as data volumes grow the only
expansion cost is hardware
• Add more nodes without a degradation in
performance

Typical Industry
• Advertising

5. Point of Sale Transaction Analysis


5. Point of Sale Transaction Analysis
• Optimizing over multiple data sources
• Utilizing information to predict demand

Typical Industry
• Retail


6. Analyzing Network Data to Predict Failure


6. Analyzing Network Data to Predict Failure
• Take the computation to the data
• Expand the range of indexing techniques from simple
scans to more complex data mining
• Better understand how the network reacts to
fluctuations
• How previously thought discrete anomalies may, in
fact, be interconnected
• Identify leading indicators of component failure
Typical Industry
• Utilities, Telecommunications,
Data Centers

7. Threat Analysis


7. Threat Analysis


• Parallel processing over huge datasets
• Pattern recognition to identify anomalies i.e. threats

Typical Industry
• Security
• Financial Services
• General: spam fighting,
click fraud

8. Trade Surveillance


8. Trade Surveillance


• Detect trading anomalies and harmful behavior

Typical Industry
• Financial services
• Regulatory bodies

9. Search Quality


9. Search Quality

• Analyzing search attempts in conjunction with
structured data
• Browsing pattern of users performing searches in
different categories

Typical Industry
• Web
• Ecommerce


10. Data “Sandbox”


10. Data “Sandbox”

• With Hadoop an organization can “dump” all this
data into a HDFS cluster
• Then use Hadoop to start trying out different
analysis on the data
• See patterns or relationships that allow the
organization to derive additional value from data

Typical Industry
• Common across all industries

Topics

• Introduction
• Summary
• Questions


Summary – 10 Common Hadoop-able Problems

1. Modeling true risk 6. Threat analysis
2. Customer churn 7. Analyzing network
analysis data to predict failure
3. Recommendation 8. Trade surveillance
engine
9. Search quality
4. Ad targeting
10. Data “sandbox”
5. PoS transaction
analysis


Who is Cloudera?

• Enterprise software & services company providing the industry’s
leading Hadoop-based data management platform
• Founding team came from large Web companies

• Products: Cloudera Enterprise & Cloudera’s Distribution for Hadoop
• All necessary packages, matched, tested and supported
• Tools to support production use of Hadoop
• The leading distribution for the enterprise

• Contributors and committers
• Fixing, patching and adding features

34

Hear More Examples @ Hadoop World 2010
http://www.cloudera.com/company/press-center/hadoop-world-nyc/

• 2nd annual event focused on practical
applications of Hadoop

• Date: October 12th 2010

• Location: Hilton New York Confirmed speakers from

• Keynote from Tim O’Reilly – founder
O’Reilly Media

• Pre and post conference training
available for Hadoop and related projects

• 36 business and technical focused sessions


Questions?


10 Common Hadoop-able Problems Webinar

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