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Introduction to Databases for Business Analytics
Week 8 Big Data 1
Term 2 2022
Lecturer-in-Charge: Kam-Fung (Henry) : Tutors:
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Acknowledgement of Country
UNSW Business School acknowledges the Bidjigal (Kensington campus) and Gadigal (City campus) the traditional custodians of the lands where each campus is located.
We acknowledge all Aboriginal and Islander Elders, past and present and their communities who have shared and practiced their teachings over thousands of years including business practices.
We recognise Aboriginal and Islander people’s ongoing leadership and contributions, including to business, education and industry.
UNSW Business School. (2022, May 7). Acknowledgement of Country [online video]. Retrieved from https://vimeo.com/369229957/d995d8087f
W8 Learning Outcomes
What is Big Data?
❑ Buzz Word!
❑ Cannot fit into a USB flash drive ❑ A large and complex dataset
❑ Social media
❑ IoT streaming of data
❑ Capturing of Media
3Vs and more Vs
Big Data is classified into three types:
❑ Structured
❑ Unstructured
❑ Semi-Structured
❑ Hadoop ❑ NoSQL
Chapter 14
Big Data and NoSQL 14-1 to 14-3
The Next Big Thing?
❑ Refers to set of data analysis and predictive analysis techniques for large and complex sets of raw data (difficult or impossible to capture in ER models).
❑ Uses machine learning and data mining techniques on raw data (instead of organizing data upfront into neat structures) to make sense of the data.
❑ Relational model: structure/schema on write
❑ Big Data model: structure/schema on read
❑ Big data emerges because:
• much larger set of data sources (e.g., Internet search/browsing, mobile devices)
• much cheaper costs to store data (e.g., costs of hard disc drives reduced substantially)
• growing interest in identifying patterns for business purposes (in all kinds of data)
• scaling out instead of scaling up
❑ Name: 7920 Disc Drive
❑ Product Number: 7920
❑ Introduced: 1977
❑ Division: Disc Memory
❑ Original Price: $17000
❑ Catalog Reference: 1979, page 641
http://hpmuseum.net/display_item.php?hw=272
3Vs and … more Vs
A Few Years Ago …
size growth
1. Volume: Quantity of data to be stored
storage issue
❑ Scaling up is keeping the same number of systems but migrating
each one to a larger system.
e.g., 100 GB to 100 TB
❑ Scaling out means when the workload exceeds server capacity, it is spread out across a number of servers.
2. Velocity: Speed at which data is entered into system and
must be processed
storage issue; data need to be processed rapidly
❑ Stream processing focuses on input processing and requires analysis of data stream as it enters the system.
❑ Feedback loop processing refers to the analysis of data to produce actionable results. (Details will be shown later.)
3. Variety: Variations in the structure of data to be stored
❑ Structured data fits into a predefined data model relational DB
❑ Unstructured data does not fit into a predefined data model
e.g., maps, images, emails, texts, tweets, videos, …
❖ Other Characteristics
❖ Variability: Changes in meaning of data based on context ❖ Sentiment analysis attempts to determine attitude
Sarcasm (does ‘good’ really mean good?
❖ Veracity: Trustworthiness of data
❖ Value: Degree of data can be analyzed for meaningful insight
❖ Visualization: Ability to graphically present data to make it understandable to users
modify prediction
Big Data Classification
Structured
Unstructured Semi-Structured
Structured Data
Any data types that clearly defined be stored, accessed and processed in a fixed format can
be defined a structured data.
A good example is data stored in a table in a normalized database. You can easily search and retrieve the data from a table using SQL tools. For instance, in the Sales_Person table, we can find the Year of Hire for Sales_Person No. 101 is 1995, Cookie Biscuit.
Sales_Person_Num
Sales_Person_Name
Year_of_Hire
Department_Num
Cookie Biscuit
Sweet Candy
Chocolate Milk
Unstructured Data
❑ Unstructured data can simply be described as not structured data; that is, anything that cannot be described as structured data.
❑ Examples of unstructured data include free text, videos, images, etc. The ability to analyze social media such as Facebook, Twitter, and WeChat, and images are among the key drives behind the growth of Big Data.
https://www.cprime.com/resources/blog/when-big-data-big/
Differences between Structured Data and Unstructured Data
https://www.datamation.com/big-data/structured-vs-unstructured-data.html
Semi-Structured
❑ Semi-Structured data is crossed between Structured Data and Unstructured Data, i.e., it has both forms of data. Examples include Electronic Data Interchange (EDI), Markup Language XML, and Open Standard JSON (JavaScript Object Notation).
❑ For example, as shown below, XML document is organized in a hierarchy with “open” and “close” tags and encoded rules that defines a human- and machine-readable format.
The Human Face of Big Data
The Human Face of Big Data
The impact of Big Data could be described the next major revolution since the Agricultural Revolution and Industrial Revolution. We can call it Digital Revolution or Big Data Revolution. Today, we have already seen large corporations, particularly the large Chinese companies, use Big Data, Artificial Intelligence, and Machine Learning extensively to drive their business strategies to gain competitiveness.
This award-winning documentary was created to explain how Big Data has evolved the way we work, shop, socialize, live, and benefit from Big Data as well as the rise of negative issues associated with Big Data. Big Data is collected, stored, and used across a wide range of products and services.
You will learn how Big Data can be used in various areas, and how Big Data influences.
The Human Face of Big Data https://www.youtube.com/watch?v=bIY3LUZ7i8Y
Warning: The music in the video is a bit loud in some sections, so you might want to test and control the volume.
Topic: Digitising Ourselves (17:36 to 23:55 of the video on previous slide)
❑ Collecting data about oneself!
❑ Pattern recognition algorithm – change the way as a society
• Personal devices, such as , Samsung Watch, and Fitbit, contain
apps and sensors used to collect data about your health (as an example). • If you have such personal devices, the question here is: can these devices
influence on how you behave? Examples can include do you pay attention to the output (such as graph or numbers) from these apps, or do you have a goal of burning number of calories per day?
Topic: Building a Global Brain (23:55 to 25:55) Topic: Creating Intelligence System (25:55 to 28:50)
Data is collected from you via devices. You react based on the data presented to you, and the action you have taken becomes another data point in this Big Data system. This becomes a cycle where the Big Data has an impact on you, and then your action becomes a data point in the Big Data.
In the video, it discusses about scheduling of buses. One of the suggestions is to be more proactive based on the needs of bus, i.e., instead of ten buses regularly travelling on one route. The bus can be diverted to another route if the demand for this particular route is reduced but a higher demand for the other route. Some would call this as building a smart city from Big Data. Thus, the city like Boston could be functioned more efficiently based on the data, i.e., “responsive to our needs”.
Topic: Targeting You (38:23 to 41:05) [1]
Target has used Big Data to identify pregnant women as part of their marketing strategy to target that segment of the consumers, provide better customer services, and improve their revenue. This practice is common among the retailers, hotel industry, airline industry and gambling industry, which offer loyalty programs to their customers as a way of rewarding them for being their customers.
The original intention of offering loyalty program is to build a customer relationship. However, in the case of Target, they use the customer information further with Big Data to create a profile of their customers who purchase products related to pregnancy and baby.
Topic: Targeting You (38:23 to 41:05)[2]
Another example is nearly all the search engines, such as Google, generate their revenue by producing advertisements based on what your searches.
Companies want to advertise their products on the Internet, and these search engine companies offer their services to the customers who search terms or phrases which meet the advertising criteria.
Topic: The Dark Side (41:06 to 45:59)
One of the criticisms on Facebook is they have been collecting data without fully reveal their intention, and how they would use your data once they collected. They can build a profile of you as an individual.
Moreover, National Security Agency (NSA) has been collecting data for a number of years.
Hadoop NoSQL
❑ De facto standard for most Big Data storage and processing
❑ Java-based framework for distributing and processing very
large data sets across clusters of computers
1. Hadoop Distributed File System (HDFS): low-level distributed file
processing system that can be used directly for data storage
2. MapReduce:programmingmodelthatsupportsprocessinglargedata
Hadoop Distributed File System (HDFS)
Based on several key assumptions
❑ High volume: default block sizes is 64 MB and can be configured to
even larger values
❑ Write-once, read-many: model simplifies concurrency issues and
improves data throughput
❑ Streaming access: optimized for batch processing of entire files as a
continuous stream of data
❑ Fault tolerance: designed to replicate data across many different
devices so that when one fails, data is still available from another device
Why we need HDFS?
HDFS enables us to
❑ deal with very large datasets
❑ solve big data problems in a distributed manner
❑ use cheap hardware rather than expensive servers
❑ have a stable data storage which is fault tolerant
❑ store data in different platforms
❑ mange data using a set of Unix-style file system commands
Acknowledgement: The slide is provided by , UNSW CSE, ZZEN9313
More in next week
Hadoop uses several types of nodes:
❑ A node is just a computer that perform one or more types of
tasks within the system
❑ Data node stores the actual file data
❑ Name node contains file system metadata
❑ Client node makes requests to the file system as needed to
support user applications
❑ Data node communicates with name node and send back
block reports and heartbeats
More in next week
Source: Poulson/lynda.com
Discussion: Data Management Models
1. File systems models
2. Relational models
3. Object-oriented models 4. Big Data models
Polyglot persistence: The coexistence of a variety of data storage and data management technologies within an organization’s infrastructure.
Source: patrickmahaney.com
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