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Assignment
Continuous Learning Activities
…
CLA #1 Tasks
Due date : Sat, 19 Mar 2022, 23:59
Required Length : 550 words max per task (there are two tasks), excluding ref list. Tolerance of 10% on word count will be applied
Marks allocated : 12.5% for CLA1
Submission procedure : via Canvas, as a .doc/.docx/.pdf fil
CLA #1 Task 1
In the essential reading for Topic 2 (Mohanty, S., Jagadeesh, M., and Srivatsa, H. (2013). Chapter 5: Application Architectures for Big Data and Analytics. In -Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics-, Apress, Berkeley), the concept of Master Data Management (MDM) is presented. MDM is often regarded as one of the key architectural component in Business Intelligence. Provide a critical discussion on why MDM is important for an effective Business Intelligence solution and the relationship between MDM to Data Warehouse and Data Lake.
CLA #1 Task 2
In the essential reading for Topic 3 (Han, J,, Pei, J., and Kamber, M. (2011), Chapter 4: Data Warehousing & Online Analytical Processing. In -Data Mining: Concepts and Techniques-. Morgan Kaufmann), the concept of ETL (Extract, Transform and Load) was briefly explained. In the evolution of BI that we now know, a concept called -Data Wrangling- or -Data Munging- is now commonly used. Provide a critical discussion comparing and contrasting the concept of ETL and Data Wrangling in the context of a Data Warehouse.
What is Continuous Learning Activities?
The Continuous Learning Activities (CLA) is a series of tasks (to be done on select weeks) requiring you to (amongst others) explore emerging or important issues/topics related to Business Intelligence.
Each CLA will have a different scope and focus, these are normally tied onto the topics of the learning materials. Critical thinking, analysis, and synthesis are highly valued; however, this should not be just an opinion piece. Evidence of readings of scholarly materials (peer-reviewed papers) is highly valued, particularly in justifying, exemplifying, and supporting your arguments, opinion, and analysis.
Continuous Learning Activities
…
CLA #1 Tasks
Due date : Sat, 19 Mar 2022, 23:59
Required Length : 550 words max per task (there are two tasks), excluding ref list. Tolerance of 10% on word count will be applied
Marks allocated : 12.5% for CLA1
Submission procedure : via Canvas, as a .doc/.docx/.pdf fil
CLA #1 Task 1
In the essential reading for Topic 2 (Mohanty, S., Jagadeesh, M., and Srivatsa, H. (2013). Chapter 5: Application Architectures for Big Data and Analytics. In -Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics-, Apress, Berkeley), the concept of Master Data Management (MDM) is presented. MDM is often regarded as one of the key architectural component in Business Intelligence. Provide a critical discussion on why MDM is important for an effective Business Intelligence solution and the relationship between MDM to Data Warehouse and Data Lake.
CLA #1 Task 2
In the essential reading for Topic 3 (Han, J,, Pei, J., and Kamber, M. (2011), Chapter 4: Data Warehousing & Online Analytical Processing. In -Data Mining: Concepts and Techniques-. Morgan Kaufmann), the concept of ETL (Extract, Transform and Load) was briefly explained. In the evolution of BI that we now know, a concept called -Data Wrangling- or -Data Munging- is now commonly used. Provide a critical discussion comparing and contrasting the concept of ETL and Data Wrangling in the context of a Data Warehouse.
What is Continuous Learning Activities?
The Continuous Learning Activities (CLA) is a series of tasks (to be done on select weeks) requiring you to (amongst others) explore emerging or important issues/topics related to Business Intelligence.
Each CLA will have a different scope and focus, these are normally tied onto the topics of the learning materials. Critical thinking, analysis, and synthesis are highly valued; however, this should not be just an opinion piece. Evidence of readings of scholarly materials (peer-reviewed papers) is highly valued, particularly in justifying, exemplifying, and supporting your arguments, opinion, and analysis.
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