INFO70286
Cloud Programming for Data Engineering
Sheridan
 
  I: Administrative Information   II: Course Details   III: Topical Outline(s)  Printable Version
 

Land Acknowledgement

Sheridan College resides on land that has been, and still is, the traditional territory of several Indigenous nations, including the Anishinaabe, the Haudenosaunee Confederacy, the Wendat, and the Mississaugas of the Credit First Nation. We recognize this territory is covered by the Dish with One Spoon treaty and the Two Row Wampum treaty, which emphasize the importance of joint stewardship, peace, and respectful relationships.

As an institution of higher learning Sheridan embraces the critical role that education must play in facilitating real transformational change. We continue our collective efforts to recognize Canada's colonial history and to take steps to meaningful Truth and Reconciliation.


Section I: Administrative Information
  Total hours: 42.0
Credit Value: 3.0
Credit Value Notes: n/a
Effective: Winter 2023
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: 20230109_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%
Total100.0%

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
Resource(s):
 TypeDescription
RequiredTextbookAWS Certified Solutions Architect Associate All-in-One Exam Guide, Joyjeet Banerjee, McGraw-Hill/Osborne © 2021, Second Edition, ISBN ISBN 9781260470185

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 
History
Comparative capabilities
Pricing
Migration
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 
OLTP vs OLAP 
Scaling
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
Sagemaker 
ML Algorithms
 
Module 12: Well-Architected Framework 
Reliability
Performance
Security
Cost optimization
Operational excellence
Auto-scaling
(Assignment #3; 25%)
 
 
 


Sheridan Policies

It is recommended that students read the following policies in relation to course outlines:

  • Academic Integrity
  • Copyright
  • Intellectual Property
  • Respectful Behaviour
  • Accessible Learning
All Sheridan policies can be viewed on the Sheridan policy website.

Appropriate use of generative Artificial Intelligence tools: In alignment with Sheridan's Academic Integrity Policy, students should consult with their professors and/or refer to evaluation instructions regarding the appropriate use, or prohibition, of generative Artificial Intelligence (AI) tools for coursework. Turnitin AI detection software may be used by faculty members to screen assignment submissions or exams for unauthorized use of artificial intelligence.

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. 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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