狠狠撸

Welcome!
? Instructor: Ruoming Jin
? Office: 264 MCS Building
? Email: jin AT cs.kent.edu
? Office hour: Mondays (4:30PM to 5:30PM) or by appointment
? TA: Xinyu Chang
? Email: xchang AT kent.edu
? Homepage: http://www.cs.kent.edu/~jin/BigData/index.html
2

Topics
? Scope: Big Data & Analytics
? Topics:
– Foundation of Data Analytics and Data Mining
– Hadoop/Map-Reduce Programming and Data Processing &
BigTable/Hbase/Cassandra
– Graph Database and Graph Analytics
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What’s Big Data?
No single definition; here is from Wikipedia:
? Big data is the term for a collection of data sets so large and complex that
it becomes difficult to process using on-hand database management tools or
traditional data processing applications.
? The challenges include capture, curation, storage, search, sharing, transfer,
analysis, and visualization.
? The trend to larger data sets is due to the additional information derivable
from analysis of a single large set of related data, as compared to separate
smaller sets with the same total amount of data, allowing correlations to be
found to "spot business trends, determine quality of research, prevent
diseases, link legal citations, combat crime, and determine real-time
roadway traffic conditions.”
4

Volume (Scale)
? Data Volume
? 44x increase from 2009 2020
? From 0.8 zettabytes to 35zb
? Data volume is increasing exponentially
6
Exponential increase in
collected/generated data

12+ TBs
of tweet data
every day
25+ TBs of
log data
every day
?
TBs
of
data
every
day
2+
billion
people on
the Web
by end
2011
30 billion RFID
tags today
(1.3B in 2005)
4.6
billion
camera
phones
world
wide
100s of
millions
of GPS
enabled
devices
sold
annually
76 million smart
meters in 2009…
200M by 2014

Maximilien Brice, ? CERN
CERN’s Large Hydron Collider (LHC) generates 15 PB a year

The Earthscope
? The Earthscope is the world's largest science
project. Designed to track North America's
geological evolution, this observatory records
data over 3.8 million square miles, amassing 67
terabytes of data. It analyzes seismic slips in the
San Andreas fault, sure, but also the plume of
magma underneath Yellowstone and much, much
more.
(http://www.msnbc.msn.com/id/44363598/ns/t
echnology_and_science-
future_of_technology/#.TmetOdQ--uI)

Variety (Complexity)
? Relational Data (Tables/Transaction/Legacy Data)
? Text Data (Web)
? Semi-structured Data (XML)
? Graph Data
? Social Network, Semantic Web (RDF), …
? Streaming Data
? You can only scan the data once
? A single application can be generating/collecting many
types of data
? Big Public Data (online, weather, finance, etc)
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To extract knowledge? all these types
of data need to linked together

A Single View to the Customer
Customer
Social
Media
Gamin
g
Entertai
n
Bankin
g
Financ
e
Our
Known
History
Purchas
e

Velocity (Speed)
? Data is begin generated fast and need to be processed fast
? Online Data Analytics
? Late decisions ? missing opportunities
? Examples
? E-Promotions: Based on your current location, your purchase
history, what you like ? send promotions right now for store next
to you
? Healthcare monitoring: sensors monitoring your activities and
body ? any abnormal measurements require immediate reaction
12

Real-time/Fast Data
? The progress and innovation is no longer hindered by the ability to collect data
? But, by the ability to manage, analyze, summarize, visualize, and discover knowledge
from the collected data in a timely manner and in a scalable fashion
13
Social media and networks
(all of us are generating data)
Scientific instruments
(collecting all sorts of data)
Mobile devices
(tracking all objects all the time
Sensor technology and
networks
(measuring all kinds of data)

Real-Time Analytics/Decision Requirement
Customer
Influence
Behavior
Product
Recommendations
that are Relevant
& Compelling
Friend Invitations
to join a
Game or Activity
that expands
business
Preventing Fraud
as it is Occurring
& preventing more
proactively
Learning why Customers
Switch to competitors
and their offers; in
time to Counter
Improving the
Marketing
Effectiveness of a
Promotion while it
is still in Play

Harnessing Big Data
? OLTP: Online Transaction Processing (DBMSs)
? OLAP: Online Analytical Processing (Data Warehousing)
? RTAP: Real-Time Analytics Processing (Big Data Architecture & technology)
16

The Model Has Changed…
? The Model of Generating/Consuming Data has Changed
17
Old Model: Few companies are generating data, all others are consuming data
New Model: all of us are generating data, and all of us are consuming
data

What’s driving Big Data
18
- Ad-hoc querying and reporting
- Data mining techniques
- Structured data, typical sources
- Small to mid-size datasets
- Optimizations and predictive analytics
- Complex statistical analysis
- All types of data, and many sources
- Very large datasets
- More of a real-time

Big Data:
Batch Processing &
Distributed Data
Store
Hadoop/Spark;
HBase/Cassandra
BI Reporting
OLAP &
Dataware house
Business Objects, SAS,
Informatica, Cognos other
SQL Reporting Tools
Interactive
Business
Intelligence &
In-memory RDBMS
QliqView, Tableau, HANA
Big Data:
Real Time &
Single View
Graph Databases
The Evolution of Business Intelligence
1990’s 2000’s 2010’s
Speed
Scale
Scale
Speed

Big Data Analytics
? Big data is more real-time in
nature than traditional DW
applications
? Traditional DW architectures
(e.g. Exadata, Teradata) are not
well-suited for big data apps
? Shared nothing, massively
parallel processing, scale out
architectures are well-suited for
big data apps
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Cloud Computing
? IT resources provided as a service
? Compute, storage, databases, queues
? Clouds leverage economies of scale of commodity
hardware
? Cheap storage, high bandwidth networks & multicore
processors
? Geographically distributed data centers
? Offerings from Microsoft, Amazon, Google, …

Benefits
? Cost & management
? Economies of scale, “out-sourced” resource management
? Reduced Time to deployment
? Ease of assembly, works “out of the box”
? Scaling
? On demand provisioning, co-locate data and compute
? Reliability
? Massive, redundant, shared resources
? Sustainability
? Hardware not owned

Types of Cloud Computing
? Public Cloud: Computing infrastructure is hosted at the
vendor’s premises.
? Private Cloud: Computing architecture is dedicated to
the customer and is not shared with other organisations.
? Hybrid Cloud: Organisations host some critical, secure
applications in private clouds. The not so critical
applications are hosted in the public cloud
? Cloud bursting: the organisation uses its own infrastructure for
normal usage, but cloud is used for peak loads.
? Community Cloud

Classification of Cloud Computing
based on Service Provided
? Infrastructure as a service (IaaS)
? Offering hardware related services using the principles of cloud computing. These could include storage services (database or disk
storage) or virtual servers.
? Amazon EC2, Amazon S3, Rackspace Cloud Servers and Flexiscale.
? Platform as a Service (PaaS)
? Offering a development platform on the cloud.
? Google’s Application Engine, Microsofts Azure, Salesforce.com’s
force.com .
? Software as a service (SaaS)
? Including a complete software offering on the cloud. Users can
access a software application hosted by the cloud vendor on pay-
per-use basis. This is a well-established sector.
? Salesforce.coms’ offering in the online Customer Relationship
Management (CRM) space, Googles gmail and Microsofts hotmail,
Google docs.

Infrastructure as a Service
(IaaS)

More Refined Categorization
? Storage-as-a-service
? Database-as-a-service
? Information-as-a-service
? Process-as-a-service
? Application-as-a-service
? Platform-as-a-service
? Integration-as-a-service
? Security-as-a-service
? Management/
Governance-as-a-service
? Testing-as-a-service
? Infrastructure-as-a-service
InfoWorld Cloud Computing Deep Dive

Key Ingredients in Cloud
Computing
? Service-Oriented Architecture (SOA)
? Utility Computing (on demand)
? Virtualization (P2P Network)
? SAAS (Software As A Service)
? PAAS (Platform AS A Service)
? IAAS (Infrastructure AS A Servie)
? Web Services in Cloud

Enabling Technology:
Virtualization
Hardware
Operating System
App App App
Traditional Stack
Hardware
OS
App App App
Hypervisor
OS OS
Virtualized Stack

Everything as a Service
? Utility computing = Infrastructure as a Service (IaaS)
? Why buy machines when you can rent cycles?
? Examples: Amazon’s EC2, Rackspace
? Platform as a Service (PaaS)
? Give me nice API and take care of the maintenance, upgrades, …
? Example: Google App Engine
? Software as a Service (SaaS)
? Just run it for me!
? Example: Gmail, Salesforce

Cloud versus cloud
? Amazon Elastic Compute Cloud
? Google App Engine
? Microsoft Azure
? GoGrid
? AppNexus

The Obligatory Timeline 狠狠撸
(Mike Culver @ AWS)
COBOL,
Edsel
Amazon.com
Darkness
Web as a
Platform
Web Services,
Resources Eliminated
Web
Awareness
Internet
ARPANET
Dot-Com Bubble Web 2.0 Web Scale
Computing

AWS
? Elastic Compute Cloud – EC2 (IaaS)
? Simple Storage Service – S3 (IaaS)
? Elastic Block Storage – EBS (IaaS)
? SimpleDB (SDB) (PaaS)
? Simple Queue Service – SQS (PaaS)
? CloudFront (S3 based Content Delivery Network – PaaS)
? Consistent AWS Web Services API

What does Azure platform offer to
developers?

Google’s AppEngine vs
Amazon’s EC2
AppEngine:
? Higher-level functionality
(e.g., automatic scaling)
? More restrictive
(e.g., respond to URL only)
? Proprietary lock-in
EC2/S3:
? Lower-level functionality
? More flexible
? Coarser billing model
June 3,
2008
Google AppEngine vs. Amazon EC2/S3
狠狠撸
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VMs
Flat File Storage
Python
BigTable
Other API’s

Topic 1: Data Analytics &
Data Mining
? Exploratory Data Analysis
? Linear Classification (Perceptron & Logistic Regression)
? Linear Regression
? C4.5 Decision Tree
? Apriori
? K-means Clustering
? EM Algorithm
? PageRank & HITS
? Collaborative Filtering

Topic 2: Hadoop/MapReduce
Programming & Data Processing
? Architecture of Hadoop, HDFS, and Yarn
? Programming on Hadoop
? Basic Data Processing: Sort and Join
? Information Retrieval using Hadoop
? Data Mining using Hadoop (Kmeans+Histograms)
? Machine Learning on Hadoop (EM)
? Hive/Pig
? HBase and Cassandra

Topic 3: Graph Database and
Graph Analytics
? Graph Database (http://en.wikipedia.org/wiki/Graph_database)
? Native Graph Database (Neo4j)
? Pregel/Giraph (Distributed Graph Processing Engine)
? Neo4j/Titan/GraphLab/GraphSQL

Textbooks
? No Official Textbooks
? References:
? Hadoop: The Definitive Guide, Tom White, O’Reilly
? Hadoop In Action, Chuck Lam, Manning
? Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer
(www.umiacs.umd.edu/~jimmylin/MapReduce-book-final.pdf)
? Data Mining: Concepts and Techniques, Third Edition, by Jiawei Han et al.
? Many Online Tutorials and Papers
42

Cloud Resources
? Hadoop on your local machine
? Hadoop in a virtual machine on your local machine
(Pseudo-Distributed on Ubuntu)
? Hadoop in the clouds with Amazon EC2

Course Prerequisite
? Prerequisite:
? Java Programming / C++
? Data Structures and Algorithm
? Computer Architecture
? Basic Statistics and Probability
? Database and Data Mining (preferred)
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This course is not for you…
? If you do not have a strong Java programming
background
? This course is not about only programming (on Hadoop).
? Focus on “thinking at scale” and algorithm design
? Focus on how to manage and process Big Data!
? No previous experience necessary in
? MapReduce
? Parallel and distributed programming

Grade Scheme
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Homework 70%
Project
Class Participation
20%
10%
? Each Class will have a sign-in sheet
? Zero-Tolerance on plagiarism

Project
? Project (due April 24th)
– One project: Group size <= 4 students
– Checkpoints
? Proposal: title and goal (due March 1st)
? Outline of approach (due March 15th)
? Implementation and Demo (April 24th and 26th)
? Final Project Report (due April 29th)
– Each group will have a short presentation and demo (15-20
minutes)
– Each group will provide a five-page document on the
project; the responsibility and work of each student shall
be described precisely
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Big Data Session 1.pptx

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Big Data Session 1.pptx