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sql代写-FIT5195
时间：2022-06-07
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
FIT5195 Data Warehousing
期末复习大纲
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
Week 1:
1. Why do we need data warehousing? Why not the operational database?
2. What is ETL?
3. Draw a star schema
Week 2:
1. What is star schema?
2. Three main components of Star Shema
3. How to identify fact measures? How to identify dimensions?
4. Why must fact measures be numerical values
5. What is an aggregated value? Why must fact measure be an aggregated value?
6. How can we check whether a simple star schema is correct?: Two Column
Methodology
7. How many different ways in creating a dimension?
8. What is the purpose of using group by when creating the fact table?
9. Avg in fact issue
10. Min or Max in the Fact
11. When need temporary fact table ?
12. Where is the PK (FK)?
13. Why must we update tempfact?
14. Why do we need to create a dimension manually?
15. When we need to create a dim by selecting data from operational database ?
16. What are attributes in dimensions?
17. When creating a fact table, why don’t we use the dimension tables?
18. When need to join dimension table with the fact table ?
Week 3:
1. Hierarchies VS Non-Hierarchies → draw a Hierarchy SS
2. Adv/Dis adv of Hierarchies
3. Wrong Hierarchies
4. Linked Dimensions
5. What is a bridge table? Why is a bridge table needed?
6. Why can’t a dimension be linked directly to the fact
7. Weight Attribute/ Weight Factor
8. Why not add the weight factor in bridge table
9. list aggregation
10. How to calculate the fact measure after averaging with weight factor?
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
Week 4:
1. Temporal Data Warehouse
2. Why is a temporal data warehousing needed?
3. Temporal Attributes VS Temporal Dimensions (with case studies)
4. What is SCD?
5. SCD types
Week 5 :
1. Determinant Dimensions
2. How to distinguish the determinant dimension with normal dimension ?
3. Determinant vs. Non VS Determinant Dimensions (case study - Olympic)
4. When to use a determinant dimension?
5. How to identify whether a dimension is a determinant dimension?
6. Medal case study: SS1 VS SS2
7. Pivot fact table
Week 6:
1. Multi-Fact Star Schemas (draw SS)
2. Can we have multiple fact measures? Do we need to separate into multi facts
3. Can dimensions be shared?
4. How to determine multi-fact?
Week 7:
1. Level of Aggregations
2. Can we have more than three levels?
3. How to lower down the level of aggregations of a star schema
4. Problems of Adding Dimensions
5. Points to consider in adding a new dimension
Week 8:
1. What is OLAP?: On-Line Analytical Processing
2. Cube
3. Rollup
4. Difference between Cube and RollUp
5. Grouping and Decode
6. Partial
7. RANK
8. Difference between RANK and DENSE_RANK
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
9. Partition
10. Cumulative Aggregate
11. BI Report
12. What is the purpose of having Business Intelligence (BI) reporting tool?
13. What is the relationship between OLAP and BI?
14. How can BI assist decision-makers in understanding the data from data warehouse?
Week 9:
1. Passive vs. Active Data Warehousing
2. Complexities in three parts
3. Two ways in creating different level SS/Dim : create table/ view
4. What to implement the automation ? trigger/view
Week 10:
1. Data Cleaning
2. What types of mistakes are there?
3. How to explore data? How to find mistakes?
4. How to correct mistakes? Should all mistakes be corrected
Week 11:
1. Regression in practice: linear/polynomial Regression
2. Classification in practice
3. Clustering in practice : centroid/density based clustering
Extra:
Chapter 8:
1. Junk & non-junk
2. When use both ?
3. Difference between them
Chapter 9:
1. Surrogate key
2. Dimension Less key
Chapter 10:
1. One attribute Dimension
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
2. Do we need it ?
3. How to deal with it (move to fact, keep in dim)
Chapter 11:
1. One pool issue in same transaction
2. One pool issue in different transaction
3. Two pool
Chapter 12:
1. Slicing fact
2. Vertical slice
3. Horizontal slice
Chapter 15:
1. Why level 0 SS ?
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
*林肯独家学习资料，版权所有·翻版必究*
FIT5195 Data Warehousing
期末复习大纲
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
Week 1:
1. Why do we need data warehousing? Why not the operational database?
2. What is ETL?
3. Draw a star schema
Week 2:
1. What is star schema?
2. Three main components of Star Shema
3. How to identify fact measures? How to identify dimensions?
4. Why must fact measures be numerical values
5. What is an aggregated value? Why must fact measure be an aggregated value?
6. How can we check whether a simple star schema is correct?: Two Column
Methodology
7. How many different ways in creating a dimension?
8. What is the purpose of using group by when creating the fact table?
9. Avg in fact issue
10. Min or Max in the Fact
11. When need temporary fact table ?
12. Where is the PK (FK)?
13. Why must we update tempfact?
14. Why do we need to create a dimension manually?
15. When we need to create a dim by selecting data from operational database ?
16. What are attributes in dimensions?
17. When creating a fact table, why don’t we use the dimension tables?
18. When need to join dimension table with the fact table ?
Week 3:
1. Hierarchies VS Non-Hierarchies → draw a Hierarchy SS
2. Adv/Dis adv of Hierarchies
3. Wrong Hierarchies
4. Linked Dimensions
5. What is a bridge table? Why is a bridge table needed?
6. Why can’t a dimension be linked directly to the fact
7. Weight Attribute/ Weight Factor
8. Why not add the weight factor in bridge table
9. list aggregation
10. How to calculate the fact measure after averaging with weight factor?
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
Week 4:
1. Temporal Data Warehouse
2. Why is a temporal data warehousing needed?
3. Temporal Attributes VS Temporal Dimensions (with case studies)
4. What is SCD?
5. SCD types
Week 5 :
1. Determinant Dimensions
2. How to distinguish the determinant dimension with normal dimension ?
3. Determinant vs. Non VS Determinant Dimensions (case study - Olympic)
4. When to use a determinant dimension?
5. How to identify whether a dimension is a determinant dimension?
6. Medal case study: SS1 VS SS2
7. Pivot fact table
Week 6:
1. Multi-Fact Star Schemas (draw SS)
2. Can we have multiple fact measures? Do we need to separate into multi facts
3. Can dimensions be shared?
4. How to determine multi-fact?
Week 7:
1. Level of Aggregations
2. Can we have more than three levels?
3. How to lower down the level of aggregations of a star schema
4. Problems of Adding Dimensions
5. Points to consider in adding a new dimension
Week 8:
1. What is OLAP?: On-Line Analytical Processing
2. Cube
3. Rollup
4. Difference between Cube and RollUp
5. Grouping and Decode
6. Partial
7. RANK
8. Difference between RANK and DENSE_RANK
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
9. Partition
10. Cumulative Aggregate
11. BI Report
12. What is the purpose of having Business Intelligence (BI) reporting tool?
13. What is the relationship between OLAP and BI?
14. How can BI assist decision-makers in understanding the data from data warehouse?
Week 9:
1. Passive vs. Active Data Warehousing
2. Complexities in three parts
3. Two ways in creating different level SS/Dim : create table/ view
4. What to implement the automation ? trigger/view
Week 10:
1. Data Cleaning
2. What types of mistakes are there?
3. How to explore data? How to find mistakes?
4. How to correct mistakes? Should all mistakes be corrected
Week 11:
1. Regression in practice: linear/polynomial Regression
2. Classification in practice
3. Clustering in practice : centroid/density based clustering
Extra:
Chapter 8:
1. Junk & non-junk
2. When use both ?
3. Difference between them
Chapter 9:
1. Surrogate key
2. Dimension Less key
Chapter 10:
1. One attribute Dimension
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
2. Do we need it ?
3. How to deal with it (move to fact, keep in dim)
Chapter 11:
1. One pool issue in same transaction
2. One pool issue in different transaction
3. Two pool
Chapter 12:
1. Slicing fact
2. Vertical slice
3. Horizontal slice
Chapter 15:
1. Why level 0 SS ?
Wechat: LincolnEduAu
*林肯独家学习资料，版权所有·翻版必究*
