Cloud Programming for Data Engineering
  I: Administrative Information   II: Course Details   III: Topical Outline(s)  Printable Version
Section I: Administrative Information
  Total hours: 42.0
Credit Value: 3.0
Credit Value Notes: n/a
Effective: Winter 2022
Prerequisites: (INFO70282 OR INFO70283)
Corequisites: N/A
Equivalents: N/A
Pre/Co/Equiv Notes: N/A

Program(s): Data Engineer
Program Coordinator(s): N/A
Course Leader or Contact: N/A
Version: 20220110_00
Status: Approved (APPR)

Section I Notes: Access to course materials and assignments will be available on Sheridan's Learning and Teaching Environment (SLATE). Students will need reliable access to a computer and the internet.

Section II: Course Details

Detailed Description
Learners will explore core cloud services such as databases, networking, storage and compute, as well as identity and access management, and cloud security. Students will distinguish different cloud service models and examine best practices for creating a well-architected cloud infrastructure that is optimized for efficiency and cost. Big Data and Machine Learning applications in the cloud will be investigated, and cloud security options will be explained.

Program Context

Data Engineer Program Coordinator(s): N/A
This course is part of the Data Engineer Micro-Credential

Course Critical Performance and Learning Outcomes

  Critical Performance:
By the end of this course, students will be able to determine the fundamental cloud services required to engineer a cloud infrastructure solution.
Learning Outcomes:

To achieve the critical performance, students will have demonstrated the ability to:

  1. Define key features of cloud services and best practices for engineering cloud solutions.
  2. Describe the functionality of the core AWS services, such as compute and storage, to select the appropriate service for any cloud requirement.
  3. Summarize the functionality of AWS' database and networking services while developing cloud infrastructure solutions.
  4. Apply AWS tools and services to execute Big Data and Machine Learning analytics projects.
  5. Practice using AWS' cloud services to develop cloud infrastructure solutions.

Evaluation Plan
Students demonstrate their learning in the following ways:

 Evaluation Plan: ONLINE
 Quiz #110.0%
 Quiz #210.0%
 Quiz #310.0%
 Assignment #120.0%
 Assignment #225.0%
 Assignment #325.0%

Evaluation Notes and Academic Missed Work Procedure:
TEST AND ASSIGNMENT PROTOCOL The following protocol applies to every course offered by Continuing and Professional Studies. 1. Students are responsible for staying abreast of test dates and times, as well as due dates and any special instructions for submitting assignments and projects as supplied to the class by the instructor. 2. Students must write all tests at the specified date and time. Missed tests, in-class/online activities, assignments and presentations are awarded a mark of zero. The penalty for late submission of written assignments is a loss of 10% per day for up to five business days (excluding Sundays and statutory holidays), after which, a grade of zero is assigned. Business days include any day that the college is open for business, whether the student has scheduled classes that day or not. An extension or make-up opportunity may be approved by the instructor at his or her discretion.

Provincial Context
The course meets the following Ministry of Colleges and Universities requirements:


Essential Employability Skills
Essential Employability Skills emphasized in the course:

  • Critical Thinking & Problem Solving Skills - Use a variety of thinking skills to anticipate and solve problems.
  • Critical Thinking & Problem Solving - Apply a systematic approach to solve problems.
  • Information Management Skills - Analyze, evaluate, and apply relevant information from a variety of sources.
  • Information Management - Locate, select, organize and document information using appropriate technology and information systems.
  • Personal Skills - Manage the use of time and other resources to complete projects.
  • Personal Skills - Take responsibility for one's own actions, decisions, and consequences.

Prior Learning Assessment and Recognition
PLAR Contact (if course is PLAR-eligible) - Office of the Registrar

  • Not Eligible for PLAR

Section III: Topical Outline
Some details of this outline may change as a result of circumstances such as weather cancellations, College and student activities, and class timetabling.
Instruction Mode: Online
Professor: N/A
RequiredTextbookAWS Certified Solutions Architect Associate All-in-One Exam Guide, Joyjeet Banerjee, McGraw-Hill/Osborne 2021, Second Edition

Applicable student group(s): Students in the online class in the Continuing and Professional Studies.
Course Details:
Module 1: Introduction to Cloud Computing 
Why Cloud  
Benefits of Cloud computing 
Cloud computing paradigm
Cloud Architecture
Deployment models
Cloud service models
Cloud constraints
Module 2: Overview of Public Cloud Services 
Comparative capabilities
Multi-cloud strategy
Hybrid cloud
Module 3: AWS infrastructure 
Regions, availability zones, local zones and outposts
Tools for managing AWS resources
Cloud Watch
Cloud Trail
Cost Explorer
(Quiz #1; 10%)
Module 4: Identity and Access Management 
IAM terminologies
Root account, users, groups, and roles
Types of policies 
Security token services
Amazon Resource Names (ARN’s)
Module 5: Amazon Storage 
Data dimensions
Block, Object and file storage
EBS (Elastic Block Storage) and S3 (Simple Storage Solution)
Storage classes
Access control
(Quiz #2; 10%)
Module 6: Amazon Compute 
Compute options 
Amazon EC2 (Elastic Compute Cloud)
Categories of compute instances
Amazon Machine Images (AMI)
EC2 Storage 
EC2 Auto scaling, accessing EC2 instances, monitoring and placement groups
Module 7: Databases in Amazon 
Relational vs non-relational databases 
DB Authentication and monitoring
DB selection criteria
(Quiz #3; 10%)
Module 8: Networking Services
Virtual private cloud: CIDR blocks, subnets, route tables
VPS Peering
Content Delivery Network: AWS CloudFront 
AWS Web Application Firewall (WAF)
(Assignment #1; 20%)
Module 9: Security 
Security Threats
Shared responsibility model
Data integrity and accountability
Data encryption
Availability and Auto-scaling
Infrastructure automation
Strategic security
Module 10: Big Data and the Cloud
Hadoop to the cloud
Data analytics tool set
Data lake requirements
Amazon Kinesis and Lambda services
Amazon Elastic MapReduce (EMR)
Big Data tools and AWS
(Assignment #2; 25%)
Module 11: Machine Learning and the Cloud
Data analytic patterns
Machine Learning infrastructure in AWS
ML Algorithms
Module 12: Well-Architected Framework 
Cost optimization
Operational excellence
(Assignment #3; 25%)

Sheridan Policies

All Sheridan policies can be viewed on the Sheridan policy website.

Academic Integrity: The principle of academic integrity requires that all work submitted for evaluation and course credit be the original, unassisted work of the student. Cheating or plagiarism including borrowing, copying, purchasing or collaborating on work, except for group projects arranged and approved by the professor, or otherwise submitting work that is not the student's own, violates this principle and will not be tolerated. Students who have any questions regarding whether or not specific circumstances involve a breach of academic integrity are advised to review the Academic Integrity Policy and procedure and/or discuss them with the professor.

Copyright: A majority of the course lectures and materials provided in class and posted in SLATE are protected by copyright. Use of these materials must comply with the Acceptable Use Policy, Use of Copyright Protected Work Policy and Student Code of Conduct. Students may use, copy and share these materials for learning and/or research purposes provided that the use complies with fair dealing or an exception in the Copyright Act. Permission from the rights holder would be necessary otherwise. Please note that it is prohibited to reproduce and/or post a work that is not your own on third-party commercial websites including but not limited to Course Hero or OneNote. It is also prohibited to reproduce and/or post a work that is not your own or your own work with the intent to assist others in cheating on third-party commercial websites including but not limited to Course Hero or OneNote.

Intellectual Property: Sheridan's Intellectual Property Policy generally applies such that students own their own work. Please be advised that students working with external research and/or industry collaborators may be asked to sign agreements that waive or modify their IP rights. Please refer to Sheridan's IP Policy and Procedure.

Respectful Behaviour: Sheridan is committed to provide a learning environment that supports academic achievement by respecting the dignity, self-esteem and fair treatment of every person engaged in the learning process. Behaviour which is inconsistent with this principle will not be tolerated. Details of Sheridan's policy on Harassment and Discrimination, Academic Integrity and other academic policies are available on the Sheridan policy website.

Accessible Learning: Accessible Learning coordinates academic accommodations for students with disabilities. For more information or to register, please see the Accessible Learning website (Statement added September 2016)

Course Outline Changes: The information contained in this Course Outline including but not limited to faculty and program information and course description is subject to change without notice. Any changes to course curriculum and/or assessment shall adhere to approved Sheridan protocol. Nothing in this Course Outline should be viewed as a representation, offer and/or warranty. Students are responsible for reading the Important Notice and Disclaimer which applies to Programs and Courses.

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