Hadoop dev 01

NYC Data Science Academy
Hadoop Application Development with Real Cases

Multi-layer Model
2

Data Pyramid and Character
 Business personnel
 ETL Engineer
 Data Warehouse Engineer
 Analyzer
 Data Visualization Engineer
 IT supporter: Operation-
Maintanence, Programmer
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Data Analysis
 Analyze collected data with statistical methods on purpose, then understand and
implement the result
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Data Mining
 Data Mining is a technique focusing on retrieving hidden information in the data. It is a process that apply
knowledge-discovery algorithms to large database and show the associations to the users.
 Original Idea: Hypothesis testing, Pattern Recognition, Artificial Intellegence, Machine Learning
 Common Data Mining Projects: Association Rules, Clustering, Outlier Analysis
 Case: Beer and Diaper
 Science: Detecting Novel Associations in Large Data Sets
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Business Intelligence
 BI = Data Warehouses (Storage) + Data Analysis and Data Mining (Analysis) +
Report (Demonstration)
 Our course
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Data Analysis Algorithms
 Popular Algorithms
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Regression
8

Time Series Analysis

Classifier
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Clustering
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Association Rules
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Data Analysis
 Data Analysis Tools
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Popular Data Analysis Tools Ranking
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Data Analysis stages
 stage 1: Dominate by Business personnel
 stage 2: Dominate by both Business personnel and Analyzer
 stage 3: Dominate by Analyzer
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Data Analysis in stage 1
 Business staff set all the requirements and most analysis plans
 According to experiences, Business staff select features, set threshold, and
IT staff search, integrate data, analyzer make report
 Feature selection and choice of threshold is based on experience and
personal knowledge
 Suitable for simple cases, analysis technique is equivalent to the simplest
decision tree
 Business staffs has valuable experiences and hard to be replaced,
analyzers are just for graphing and is easily replaced
 This is common in the traditional industry
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 More complex. Business staffs could analyze a small number of
data records while cannot figure out all the features and the
relationship among them. They have no experience with large
number of samples.
 Analyzer come to clean data and select features, and finally build
suitable model to solve problem.
 Business staffs and analyzer could evaluate the result together,
very likely to success. Analyzer prefer this step because their ability
and value is confirmed.
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Spammer in Wordpress

 Business staffs have no experience for the
case, and cannot offer any useful prior
knowledge
 Data analyzers use various tools and models to
mine the data and trying to have interesting
discovery
 It is analyzer’s ideal world, while it is likely to
fail
 Business staffs cannot get involved, and they
dislike this stage
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Step Forward
 The first stage(Gold on the ground) -> The second
stage(Gold beneath the ground) -> The third stage (Gold
deeply buried)
 If analyzers are reckless, business staffs will resist to
help
 Data analysis is rooted in the business background. The
goal of analysis is increasing profit. Successful analysis
could not be apart from business
 Interesting topic is more important than the model
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What is Big Data

Features of Big Data

Challenges for Analyzers
 Bottleneck for both insertion and query due to the increasing amount of data
 The trend of integrating users’ application and analysis result is asking for faster
real-time computation and response time
 More complex models require more expensive computation
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Dilemma of Traditional Data Analysis
Tools
 R, SAS, SPSS are experimental tools
 Capable data size is restricted by the memory size
 Use Oracle database for large volume of data, but lack of professional and fast
analyzing ability
 Sampling is a limited solution, it is not useful for clustering and recommendation
system
 Solution: Hadoop cluster and Map-Reduce parallel computing
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Case 1: analysis and monitor for a
telecommunication company
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Case 1: analysis and monitor for a
telecommunication company
 Configuration of the original database server: HP minicomputer, 128G memory, 48-
core CPU, RAC with two nodes, one node for insertion and the other for query
 Storage: HP virtual storage, over 1000 disks
 Architecture: Oracle RAC with two nodes
 Bottleneck: 1. Insertion 2. Query
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Case 2: DNA database
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Case 3: Social analysis, activity
fingerprint detection

28|
Public Voice
mail intersect IMSI 1 IMSI 2 …… IMSI n
total call
duration
User A IMSI 20% 12% …… 5% 365
User B IMSI 15% 13% …… 2% 310
Public SMS
intersect IMSI 1 IMSI 2 …… IMSI n
Monthly
SMS count
User A IMSI 50% 10% …… 5% 200
User B IMSI 20% 13% …… 2% 260
Public base
station CGI 1 CGI 2 …… CGI n Shutdown
User A IMSI 20% 12% …… 5% 20%
User B IMSI 15% 13% …… 2% 5%
Public Fingerprint
(0.2, 0.12, …, 0.05)
(0.15, 0.13, …, 0.02)
(0.5, 0.1, …, 0.05)
(0.2, 0.13, …, 0.02)
(0.2, 0.12, …, 0.05, 0.2)
(0.15, 0.13, …, 0.02, 0.05
eigenvector


When equals to , these two vectors are independent
When equals to 0 , these two vectors are perfectly dependent
The closer is from 0, the more dependent these vectors are
90
Case 3: Social analysis, activity
fingerprint detection
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Case 3: Social analysis, VIP detection
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Solution that analyzers look forward to
 Perfectly eliminate the bottleneck in the foreseeable future
 Smoothly transplant available techniques, for example SQL and R.
 The cost of new platform: hardware and software, re-development, skill training,
maintenance
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Path to Big Data

Idea of Hadoop
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Map-Reduce Programming
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Map-Reduce program for meteorological
data analysis
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Map-Reduce implementation for popular
algorithms
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Map-Reduce implementation for popular
algorithms
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Why not Hadoop？
 Java?
 Hard to control?
 Hard to integrate data?
 Hadoop vs Oracle
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Analysis under Hadoop system
 Mainstream: Java program
 Light-weighted script language: Pig
 Smooth transplant from SQL: Hive
 NoSQL: HBase
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Family of Hadoop
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pig
 Pig could be treated as a client software
to the hadoop, could connect to hadoop
and analyze
 Pig is convenient for users unfamiliar
with java, using a SQL-like language,
pig latin, dealing with data flow
 Pig latin could perform sorting, filtering,
sum, grouping, association, and define
custom functions. It is a light-weighted
script language for data operation and
analysis
 Pig could be treated as the mapping
from pig latin to map-reduce
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Hive
 Data warehouse tool, could turn
primary data structure in Hadoop into
tables in Hive
 Support HiveQL, a language almost
the same as SQL, its function is the
same as SQL except updating,
indexing and
 could be treated as the mapping from
SQL to map-reduce
 Offering interfaces for shell、
JDBC/ODBC、Thrift、Web
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Features of Mahout
 Mahout is for scalable machine learning
algorithms (M-R implementation), and
Hadoop platform is not necessary. The
core library also have efficient algorithms
on single machine
 Mature and popular algorithms are
1. Frequent Itemset Mining
2. Clustering
3. Classifier
4. Recommendation System
5. Frequent Subgraph Mining
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Reference Textbooks

Reference Textbooks
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Typical Experiment Environtment(with
server)
 Server: ESXi, capable of deploying multiple virtual machines and could run 3
machines at the same time
 PC: Linux or Windows+Cygwin, linux could be standalone or a virtual machine
 SSH: Use command ssh under linux, and SecureCRT or putty under Windows to
connect with remote linux server
 Vmware client: Management of ESXi
 Hadoop: Use version 1.x or 2.x
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Typical Experiment Environtment(with
only PC or laptop running Windows)
 At Least 4G memory, 64bit windows is preferred, because 32bit machine can use
only more than 3G memory.
 Install vmware workstation or virtual box
 Deploy 3 virtual machines and running at the same time. If can only run two VMs,
treat host as a node (by cygwin), and use bridged networking for virtual network
 Install Linux and Java
 Old computers could consider pseudo-distributed environment
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Experiment Environment
 Deploy Pig
 Deploy Hive
 Deploy Mahout

List of Cases of the Course
 Analysis of high volume website log system; Retrieve KPI data(Map-Reduce)
 LBS application for telecommunication company; Analysis of trace of user‘s mobile phone(Map-
Reduce)
 User analysis for telecommunication company; Labeling duplicated users by the fingerprint of
calls(Map-Reduce)
 Recommendation system for E-commerce company(Map-Reduce)
 Complicated recommendation system application(mahout)
 Social network; Distance between users; Community detection(Pig)
 Importance of nodes in a social network(Map-Reduce)
 Application of clustering algorithm; Analysis of VIP(Map-Reduce, Mahout)
 Financial data analysis; Retrieve reverse repurchase information from historical data(Hive)
 Set stock strategies with data analysis(Map-Reduce, Hive)
 GPS application; Sign-in data analysis(Pig)
 Implementation and optimization of sorting on Map-Reduce
 Middleware development; Cooperation of multiple Hadoop clusters

Hadoop dev 01

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