MONASH
INFORMATION TECHNOLOGY
Where To?
Exam Preparation
FIT2094-FIT3171
Operational Database – the unit’s focus
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Application Development
▪ Web based front ends
– Wide range of approaches: PHP, ASP.NET, etc
– Very Rudimentary (requires VPN)
▪ PL/SQL
– backend development
– Triggers, functions, procedures and packages
– Procedure to change employee departments: move_employee
(empno, new dept)
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FIT2104 – Web database interface
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Usage of database
▪Example of a supermarket ▪Decision making
–Operational level
•How often do we need to re-stock X-item?
–Strategic and tactical level
•Is there any branch that performs worse than the state average?
•What is the total sales made by each state each year and across a number of years?
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Operational Data vs. Decision Support Data
▪ Operational data
– Mostly stored in relational database
– Optimized to support transactions representing daily operations
– Example:
– How many students enrolled in FIT2094?
▪ Decision support data differs from operational data in three main areas:
– Time span
– Granularity
– Dimensionality
– Example:
– What is the total number of students in the foundation units in each year (subtotal of the two semesters numbers) and the total across years, across a single unit.
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Decision Support Database Requirements
Specialized DBMS tailored to provide fast answers to complex queries Three main requirements
– Database schema
– Data extraction and loading
– Database size
Database schema
– Complex data representations
– Aggregated and summarized data
– Queries extract multidimensional time slices
Data extraction and filtering
– Supports different data sources
• Flat files
• Hierarchical, network, and relational databases
• Multiple vendors
– Checking for inconsistent data
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The Data Warehouse
▪ Database size
– In 2013, eBay had around 90 Petabytes of data in its
data warehouses (90,000 Terabytes)
– DBMS must support very large databases (VLDBs)
▪ Integrated, subject-oriented, time-variant, and nonvolatile collection of data
– Provides support for decision making
▪ Usually a read-only database optimized for data analysis and query processing
▪ Requires time, money, and considerable managerial effort to create
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FIT3003 – Business intelligence and data warehousing
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IOT – the explosion – Data, Data, Data …..
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Data Growth
Source: https://www.domo.com/learn/data-never-sleeps-7#/
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Data Growth
Source: https://www.seagate.com/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
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Big Data Characteristics
▪ Volume
– The quantity of data to be stored
▪ Velocity
– The speed at which data enters the system and must be processed
▪ Variety
– Variations in the structure of the data to be stored
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Big Data Characteristics: Volume
Scaling up: keeping the same number of systems but migrating each one to a larger system
Scaling out: when the workload exceeds server capacity, it is spread out across a number of servers
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Scaling
▪ How do we scale current relational systems? SQL designed for database as a single physical entity
– Purchase bigger “boxes”: costly and has real limits
– Increase the number of processors, yielding parallel computation/database with complex issues to handle
– Distribute database – challenges to maintain ACID transaction principles and issues of availability/consistency
▪ The rise of OO programming in the 80’s also highlighted a problem known as the “Impedance Mismatch”
– The program treats items as objects, but they need to be mapped to relational tables (“de aggregating” the object)
– Also issues about “private” vs “public” (relational about need, OO absolute characteristic of data)
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Scaling continued
▪ Big players, notably Google and Amazon chose a different path
– Lots and lots of smaller boxes (“commodity” servers)
– Non relational structure
– Google: Bigtable
• https://research.google/pubs/pub27898/
• https://cloud.google.com/bigtable/docs/overview
• Used for wide range of apps Gmail, Google Earth, YouTube
– Amazon: Dyanmo
• http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf
• Based on Dynamo: https://aws.amazon.com/dynamodb/
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Scaling continued
▪ Term “NoSQL” coined by John Oskarsson in 2009 after calling a …”free meetup about “open source, distributed, non relational databases” or NOSQL for short”…
– http://blog.oskarsson.nu/post/22996139456/nosql-meetup
▪ Characteristics
– Non relational,
– mostly open source,
– distributed (cluster friendly),
– schema-less (no fixed storage schema)
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Big Data Characteristics: Velocity
▪ Stream processing: focuses on input processing and requires analysis of data stream as it enters the system
– CERN Large Hadron Collider 600TB per second 1 GB per second ▪ Feedback loop processing: analysis of data to produce
actionable results
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Fast Data Processing
▪ Computer systems
–Parallel computer: A single machine with massive number of CPUs.
–Cluster of computers: Multiple machines connected via network; Commodity computer.
▪ Database structure
–Non-relational database (NoSQL)
•No update, append only. Optimised for a ’main’ operation
•Examples: MongoDB, Cassandra –Distributed File Systems
•HDFS (Hadoop File Systems) / Parquee File Systems ▪ Parallel data processing
–Hadoop / Spark ▪ In Memory database
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Big Data Characteristics: Variety
Structured data: fits into a predefined data model
– Relational databases
– Incoming data decomposed under normalisation rules to fit the data model
Unstructured data: does not fit into a predefined model
– Big Data requires that the data is captured in its natural format as generated without imposing a data model on it
Semi structured data: combines elements of both
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FIT3176 – Advanced database design
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Data Processing Ecosystem
http://www.clearpeaks.com/blog/big-data/big-data-ecosystem-spark-and-tableau
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“Horses for Courses”
▪ Conventional RDBMS will continue play an important and significant role in OLTP (Online Transactions Processing)
▪ Increasingly now a range of database products are available, need to select appropriate product/model for task at hand.
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FIT2094-FIT3171 Exam
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2021 Semester Summer B Exam Format
▪ Timed: 2 hours 10 minutes (reading time included)
▪ e-exam platform: https://eassessment.monash.edu/
▪ Close book (no cheat sheets), e-invigilated
▪ Learn more here:
https://www.monash.edu/exams/electronic-exams/about
– read:
• Supervised eExams using Monash eVigilation
• eExams requiring handwritten answers
For the modelling question makes use of a hybrid question
– write answer on paper (mark answered on e-exam)
– photograph with phone
– upload via QR code (after exam has been completed) – important that
you practice this process
– and Try out a general knowledge practice exam
▪ Note that the exam is a time-pressured test
– manage your time wisely
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2021 Summer B Exam Format
▪ 100 marks 50% of your final mark in FIT2094-FIT3171. – Minimum to pass the unit overall:
• 40% in-semester, 40% exam and 50% overall
– Assignment 2 marking will not be finalised before the exam
▪ Questions:
– 6 parts – cover theory and application
– Timing is crucial – 100 marks, 120 mins – 1 mark/minute target
– Part D – SQL Case Study will be released 9am the day before the exam day.
• No tables/data provided on FITUGDB, no access to SQL Developer, LucidChart and other softwares
▪ Exam when?
– your responsibility
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2021 SSB Mock Exam
▪ Link is provided on Exam tab Moodle, self enrolled.
– timed (2 hrs 10 mins), unlimited attempts
– do not open or attempt this mock exam until such time as you are able to make a full 2 hrs and 10 mins attempt, rather than open and browse the paper.
▪ Available from Thursday 11th February at 12PM, sample solution will be available Monday 15th February from 3 PM
▪ Serves to provide a general overview of the general structure of the exam only.
▪ To protect the integrity of the exam: NO ACTUAL EXAM QUESTIONS are included; and the COMPOSITION OF THE QUESTIONS IN EACH PART are SUBJECT TO CHANGE.
▪ All content specified by the Unit Guide is examinable, including but not limited to
– Pre-reading (weekly Coronel & Morris chapters)
– Workshop Slides and Videos
– Tutorial Notes, and
– all other Moodle Materials (except where explicitly stated NOT EXAMINABLE).
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Workshop Session 2 and 5 – Data Modelling INCLUDING BUT NOT LIMITED TO THESE TOPICS…
▪Conceptual vs Logical Level
▪Entity
–Strong vs weak
–Associative entity
▪Types of attributes
▪Relationship
–Type : one-to-one, one-to-many, many-to-many
–Cardinality and Participation –Identifying vs Non-identifying.
▪Mapping from Conceptual to Logical –E.g. Mapping many-to-many
▪FIT3171 – UML
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Workshop Session 3 – Relational Model INCLUDING BUT NOT LIMITED TO THESE TOPICS…
▪Relational model properties.
▪Keys
–Superkey, Candidate Key, Primary Key –Foreign Key
▪Data Integrity –Entity integrity –Referential Integrity
▪Relational Algebra –Understanding of efficiency
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Workshop Session 4 – Normalisation INCLUDING BUT NOT LIMITED TO THESE TOPICS…
▪UNF to 3 NF –Mapping form to UNF
–UNF to 1 NF – remove repeating group. –1NF to 2 NF – remove partial dependency. –2NF to 3NF – remove transitive dependency.
▪Dependency diagrams
–Use the general definition
–Partial in 1NF, Transitive in 2NF, Full in 3NF
•use this notation: cust_id → cust_name, cust_address
▪Be careful in choosing the PK!
▪Mapping a set of 3NF relations to a logical model
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Workshop Sessions 6 and 8– DDL and DML
INCLUDING BUT NOT LIMITED TO THESE TOPICS…
▪DDL
–CREATE TABLE statements
•Primary key definition •Foreign key definition •Other Constraints
–ALTER
–DROP ▪DML
–INSERT
•Adherence to referential integrity constraints and the order of insertion –Oracle Sequence
–UPDATE (DML) –DELETE (DML) –COMMIT
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Workshop Session 7, 9 and 10 – SQL INCLUDING BUT NOT LIMITED TO THESE TOPICS…
▪Single table retrieval with predicate
▪Join
–Natural join
–Outer join ▪Aggregate functions ▪Set Operators ▪Subquery
▪Oracle functions
TO_CHAR, TO_DATE, NVL, UPPER, LOWER, ROUND
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Workshop Session 9 PLSQL and Workshop Session 11 DB Connectivity, Web Technology
INCLUDING BUT NOT LIMITED TO THESE TOPICS…
▪Web database connectivity
–Understanding of the principles and ALL core concepts:
•Database middleware
•Web to database middleware
•Using PHP to communicate with databases
–must understand php code which relates to database –Database design frameworks
•modern frameworks
•ORM
–Security → SQL Injection
▪FIT3171 – TRIGGER
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Workshop Session 8 – Transaction Management INCLUDING BUT NOT LIMITED TO THESE TOPICS…
▪Transaction.
▪ACID properties.
▪Transaction problems.
▪Transaction management with locks.
▪Wait For Graphs
▪Restart and Recovery using Transaction Log.
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Workshop Session 12
▪The content of Session 12’s workshop –Database Trends
–Future directions
Is NOT examinable (questions relate to this session’s new content will not appear on the exam)
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Consultations for Final Exam
▪ From Tuesday 16th February 2021, one online consultation session per day will be provided.
– Details posted on Moodle
▪ Please don’t come to consultations in a hope to squeeze some useful information about final exam
– Session intended to clear up any issues YOU find as you prepare for the exam
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