Big Data

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First found May 22, 2018

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CS525: Special Topics in DBs
Large-Scale Data Management
Introduction & Logistics
Spring 2013
WPI, Mohamed Eltabakh
1
Theme of this Course
Large-Scale Data Management
Big Data Analytics
Data Science and Analytics
• How to manage very large amounts of data and extract value and
knowledge from them
2
Introduction to Big Data
What is Big Data?
What makes data, “Big” Data?
3
Big Data Definition
• No single standard definition…
“Big Data” is data whose scale, diversity, and
complexity require new architecture, techniques,
algorithms, and analytics to manage it and extract
value and hidden knowledge from it…
4
Characteristics of Big Data:
1-Scale (Volume)
• Data Volume
• 44x increase from 2009 2020
• From 0.8 zettabytes to 35zb
• Data volume is increasing exponentially
Exponential increase in
collected/generated data
5
Characteristics of Big Data:
2-Complexity (Varity)
• Various formats, types, and structures
• Text, numerical, images, audio, video,
sequences, time series, social media
data, multi-dim arrays, etc…
• Static data vs. streaming data
• A single application can be
generating/collecting many types of
data
6
Characteristics of Big Data:
3-Speed (Velocity)
• 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
7
Big Data: 3V’s
8
Some Make it 4V’s
9
Harnessing Big Data
•
OLTP: Online Transaction Processing (DBMSs)
•
OLAP: Online Analytical Processing (Data Warehousing)
•
RTAP: Real-Time Analytics Processing (Big Data Architecture & technology)
10
Who’s Generating Big Data
Mobile devices
(tracking all objects all the time)
Social media and networks
(all of us are generating data)
Scientific instruments
(collecting all sorts of data)
Sensor technology and networks
(measuring all kinds of 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
11
The Model Has Changed…
• The Model of Generating/Consuming Data has Changed
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
12
What’s driving Big Data
- Optimizations and predictive analytics
- Complex statistical analysis
- All types of data, and many sources
- Very large datasets
- More of a real-time
- Ad-hoc querying and reporting
- Data mining techniques
- Structured data, typical sources
- Small to mid-size datasets
13
Value of Big Data Analytics
• Big data is more real-time in nature
than traditional DW applications
• Traditional DW architectures (e.g.
Exadata, Teradata) are not wellsuited for big data apps
• Shared nothing, massively parallel
processing, scale out architectures
are well-suited for big data apps
14
Challenges in Handling Big Data
• The Bottleneck is in technology
• New architecture, algorithms, techniques are needed
• Also in technical skills
• Experts in using the new technology and dealing with big data
15
What Technology Do We Have
For Big Data ??
16
17
Big Data Technology
18
What You Will Learn…
• We focus on Hadoop/MapReduce technology
• Learn the platform (how it is designed and works)
• How big data are managed in a scalable, efficient way
• Learn writing Hadoop jobs in different languages
• Programming Languages: Java, C, Python
• High-Level Languages: Apache Pig, Hive
• Learn advanced analytics tools on top of Hadoop
• RHadoop: Statistical tools for managing big data
• Mahout: Data mining and machine learning tools over big data
• Learn state-of-art technology from recent research papers
• Optimizations, indexing techniques, and other extensions to Hadoop
19
Course Logistics
20
Course Logistics
• Web Page: http://web.cs.wpi.edu/~cs525/s13-MYE/
• Electronic WPI system: blackboard.wpi.edu
• Lectures
• Tuesday, Thursday: (4:00pm - 5:20pm)
21
Textbook & Reading List
• No specific textbook
• Big Data is a relatively new topic (so no fixed syllabus)
• Reading List
• We will cover the state-of-art technology from research papers in big
conferences
• Many Hadoop-related papers are available on the course website
• Related books:
• Hadoop, The Definitive Guide [pdf]
22
Requirements & Grading
•
Seminar-Type Course
• Students will read research papers and present them (Reading List)
•
Hands-on Course
• No written homework or exams
• Several coding projects covering the entire semester
23
Done in teams
of two
Requirements & Grading (Cont’d)
•
Reviews
• When a team is presenting (not the instructor), the other students should prepare a
review on the presented paper
• Course website gives guidelines on how to make good reviews
• Reviews are done individually
24
Late Submission Policy
• For Projects
•
•
•
•
•
One-day late  10% off the max grade
Two-day late  20% off the max grade
Three-day late  30% off the max grade
Beyond that, no late submission is accepted
Submissions:
• Submitted via blackboard system by the due date
• Demonstrated to the instructor within the week after
• For Reviews
• No late submissions
• Student may skip at most 4 reviews
• Submissions:
• Given to the instructor at the beginning of class
25
More about Projects
•
A virtual machine is created including the needed platform for the projects
• Ubuntu OS (Version 12.10)
• Hadoop platform (Version 1.1.0)
• Apache Pig (Version 0.10.0)
• Mahout library (Version 0.7)
• Rhadoop
• In addition to other software packages
•
Download it from the course website (link)
• Username and password will be sent to you
• Need Virtual Box (Vbox) [free]
26
Next Step from You…
1. Form teams of two
2. Visit the course website (Reading List), each team selects
its first paper to present (1st come 1st served)
•
Send me your choices top 2/3 choices
3. You have until Jan 20th
•
Otherwise, I’ll randomly form teams and assign papers
4. Use Blackboard “Discussion” forum for posts or for
searching for teammates
27
Course Output: What You
Will Learn…
• We focus on Hadoop/MapReduce technology
• Learn the platform (how it is designed and works)
• How big data are managed in a scalable, efficient way
• Learn writing Hadoop jobs in different languages
• Programming Languages: Java, C, Python
• High-Level Languages: Apache Pig, Hive
• Learn advanced analytics tools on top of Hadoop
• RHadoop: Statistical tools for managing big data
• Mahout: Analytics and data mining tools over big data
• Learn state-of-art technology from recent research papers
• Optimizations, indexing techniques, and other extensions to Hadoop
28
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