Question

1. Describe the IMPACT cycle. Why does its order of the processes and its recursive nature make sense? 2. What is the purpose of a data dictionary? Identify four different attributes that could be stored in a data dictionary and describe the purpose of each. 3. In the ETL process, one important step to process when transforming the data is to work with NULL, N/A, and zero values in the dataset. If you have a field of quantitative date, what would the effect of the following? a. Transforming NULL and N/A values into blanks b. Transforming NULL and N/A values into zeros c. Deleting records that have NULL and N/A values from your dataset

Answer 1

1) Describe the IMPACT cycle. Why does its order of the processes and its recursive nature make sense?
Solution :-

Actually IMPACT Cycle is nothing but of the process that contened you have to secifiy your goal , also there are 3 imp role is ,
1. Identify
2. Learn
3. Improve

firstally you have learn about any thing you have to done with any person , after that you have to accept the orginal things you learn that subjects or what ever you learn then you have to know the reality of that subject after that you set your personal goal to achive that goal any how in the current senario.

second part of IMPACT Cycle is you have accept the real things , then you will work properally. That content you to study the eqally to all points after that you have any choice or not for that subject that is also important. Again You learn to help of voice learning.

The last part is Confirm your direction after that to check your review progress and then you will improve your invent and then you will go to your final planning action regards to any Subject.

2. What is the purpose of a data dictionary? Identify four different attributes that could be stored in a data dictionary and describe the purpose of each.
Solution:-

A Structure place to keep details of the contenct of data flow process and data flow, Actually its also know as data about data.

It content 4 parts ,

1. Data Element.

   It is nothing but smallest unit of data and its nothing but data of data.

2. Data Structure.

   It is nothing but group of data element and its nothing but data of data.  

3. Data Flow.

   It is nothing but group of data Structure and its nothing but data of data.      

4. Data Stores.

   It is nothing but group of data Structure and its nothing but data of data.      

3) In the ETL process, one important step to process when transforming the data is to work with NULL, N/A, and zero values in the data set. If you have a field of quantitative date, what would the effect of the following?

a. Transforming NULL and N/A values into blanks.
Solution :-

Incorporating NULL into the relational data warehouse can vastly complicate querying and cause much end-user confusion. If IT specialists (programmers, developers, and DBA) can’t grasp the idea or importance of NULL, then how can we expect non-technical end users to understand NULL? We must assume that business end users are querying the data warehouse for statistical information; how will NULL affect the answers they get from the data? If the end user queries the warehouse and filters on a null able value, is it valid to leave the entire record out of the analysis? Would this matter? If a value is unknown at the time of measurement, which is a plausible condition with slowly-changing dimensions, would it be better to omit the record from the analysis, or should you include it with some sort of zero or “temporarily unknown” condition. How would you do that? If a value exists in the source database but for some reason the extraction, transformation, and loading (ETL) process failed to bring it into the data warehouse, is it better to treat that value as NULL (the true condition) or should you calculate and assign a median or average value to it?

b. Transforming NULL and N/A values into zeros.
Solution :-

Step 1: Filter your column's null values into a separate branch using a Filter Row action.

Step 2: Add an Add Constants action to the null branch that adds a new column with whatever name (Zero Value is what I used) with a value set to 0.

Step 3: Add a Select Columns action to the null branch and select the columns you need except the column that holds the null values (the column you set the filter on in Step 1).

Step 4: Left join your filtered branch with your input data set on your unique key.

Step 5: Combine the original column on which you set the filter in Step 1 with the constant column you added to the null rows in Step 2 use a Combine Columns action.

c. Deleting records that have NULL and N/A values from your data set.
Solution :-

As a side note, I sometimes prefer to talk about a "reporting database" rather than a "data warehouse", because it keeps things in perspective. Some DBAs and developers start making plans for huge server farms and multi-year ETL projects as soon as they hear the words "data warehouse", but in the end it's just a reporting database.)

Anyway, it isn't completely clear where you want to use NULL but it looks like it may be an attribute on a dimension.

I (probably) wouldn't use any of your three approaches, but it depends on the meaning of your data. Importing the data as-is is not useful because part of the value of a data warehouse is that the data has been cleaned and is consistent, which makes querying and comparing data along other dimensions much easier.

Replacing empty strings with 'Unknown' may or may not be correct: what does an empty string mean in the source system? There's a big difference between "it means there's no suburb" and "it means we don't know if there's a suburb". Assuming that an empty string means "no suburb" and NULL means "unknown" then I would import the empty strings as they are, but replace NULL with 'Unknown'. The main reason for doing that is that if the Suburb field will be used as a filter condition in a report, it's easier for users (and possibly your reporting tool) to work with a non-NULL value like 'UNKNOWN'. And if there is no consistency in the source system and you don't know what empty strings and NULLs mean, then you need to clarify that first and ideally fix the source system too (another benefit of a DWH is that it helps to identify inconsistencies and data handling errors in source systems).

Your last idea to convert NULLs to empty strings is the same issue: what does a NULL actually mean in the source system? If it means "no suburb" then replacing it with an empty string is probably a good idea, but if it means something else then you should handle it as something else.

So to summarize, my preference would be to import empty strings as-is, and convert NULL to 'UNKNOWN', but I can't be sure that this actually makes sense in your case. There's no single answer to this question because it all depends on your specific data and what it means. But there's no problem with using NULL in a data warehouse (or any other database) as long as you do it consistently and with a clear understanding of how the source systems handle data.