The document discusses the application of big data in various industries, presenting use cases such as telecommunications, fraud detection, and credit card transactions. It emphasizes the evolution of tools and processing methods like Hadoop, MapReduce, and machine learning techniques for extracting business value from data. Examples highlight proactive monitoring and pattern recognition to drive efficiency and insights in diverse sectors.

1. Big Data Use
DevNexus Conference
2/18/2013
*Fully buzzword-compliant title
1
*
Cases

2. whoami
•
Brad Anderson
•
Solutions Architect at MapR (Atlanta)
•
ATLHUG co-chair
•
NoSQL East Conference 2009
•
“boorad” most places (twitter, github)
•
banderson@maprtech.com
2

3. Mobile
Virtualization
Social
Media
B2B
Application Service Provider
Cloud
Client/Server
Web 2.0
Service Bureau
Software-as-a-Service
3

4. BIG DATA
4

5. 5

6. Business Value
6

7. Business Value
7

8. Big Data is not new!
but the tools are.
8

9. Ship the Function to the Data
Distributed Computing
Traditional Architecture
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RDBMS
function
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data
data
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data
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function
function
function
data
data
data
data
data
data
data
data
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function
function
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data
data
data
SAN/NAS
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10. Variation: Multiple MapReduces
Example: Fraud Detection in User Transactions
MapReduce
Transaction
data
LDA training
LDA scoring
G2 score
95 %-ile LDA anomaly
HBase /
MapR M7 Edition
Candidate events
for analyst review
http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
10

11. MapR Distribution for Apache Hadoop

Complete Hadoop
distribution

Comprehensive
management suite

Industry-standard
interfaces

Enterprise-grade
dependability

Higher performance
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12. Big Data Ecosystem
12

13. Use Case
Company
 Data Source(s)
 Technique(s)
 Business Value

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14. Proactive Monitoring
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15. Data Sources
Server Telemetry
 Monitoring Logs
 Network Flow

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16. Techniques
Pattern Recognition
 Proactive Monitoring
 Early Alert Delivery

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17. Business Value
17

18. Telecommunications Giant
ETL Offload
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19. Telecommunications
Data Sources
Customer Records
 Contract Data
 Purchase Orders
 Call Center

19

20. Telecommunications
Techniques
Analytics
ETL
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21. Telecommunications
Techniques
+
ETL (Hadoop)
Analytics (Teradata)
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22. Telecommunications
Business Value
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23. Credit Card
Issuer
Data Sources
Customer Purchase History
 Merchant Designations
 Merchant Special Offers

23

24. Credit Card
Issuer
Techniques
Hadoop
Purchase
History
Export
(4 hrs)
App
App
Merchant
Information
Recommendation
Engine Results
(Mahout)
Presentation
Data Store
(DB2)
App
App
Merchant
Offers
App
Import
(4 hrs)
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25. Credit Card
Issuer
Techniques
Hadoop
Purchase
History
Merchant
Information
Recommendation
Engine Results
(Mahout)
Index
Update
(2 min)
App
App
Recommendation
Search Index
(Solr)
App
App
Merchant
Offers
App
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26. Credit Card
Issuer
Business Value
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27. Waste & Recycling Leader
Idle Alerts
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28. Data Sources

Truck Geolocation Data
20,000 trucks
– 5 sec interval
–

Landfill Geographic Boundaries
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29. Techniques
Realtime Stream Computation
(Storm)
Truck
Geolocation
Data
Hadoop
Storage
Immediate
Alerts
Batch Computation
(MapReduce)
Tax Reduction
Reporting
Shortest Path
Graph Algorithm
Route
Optimization
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30. Business Value
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31. Fraud Detection
Data Lake
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32. Data Sources


Anti-Money Laundering
Consumer Transactions
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33. Techniques
Anti-Money Laundering
System
Consumer Transactions
System
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34. Techniques
AML
Data Lake
(Hadoop)
Suspicious
Events
Consumer
Transactions
Analyst
Latent Dirichlet Allocation,
Bayesian Learning Neural Network,
Peer Group Analysis
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35. Business Value
35

36. Machine Learning
Search Relevance
DNA Matching
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37. Data Sources
Birth, Death, Census, Military, I
mmigration records
 Search Behavior Activity
 DNA SNP (snips)

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38. Techniques
Record Linking
 Search Relevance
 Clickstream Behavior
 Security Forensics
 DNA Matching

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39. Business Value
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40. Traffic Analytics
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41. Data Sources

Inrix Road Segment Data
Avg Speed / minute / segment
– Reference Speeds
–

Road Segment Geolocation Data
41

42. Techniques
 Bottleneck Detection Algorithm
 Time Offset Correlations
–

Alternate Routes
Predictive Congestion Analysis
–
Growth & Term Assumptions
42

43. 43

44. 44

45. Business Value
45

46. Similar Characteristics
Lots of Data
 Structured, Semi-Structured, Unstructured
 Varied Systems Interoperating
– Hadoop, Storm, Solr, MPP, Visualizations

Increase Revenue
 Decrease Costs

46

47. Thank You
47