UMGC IFSM 330 Candy Assignment

Attachments:

· 2017_product_data_students-final.csv

· 2018_product_data_students-final.csv

· 2019_product_data_students-final.csv

· Candy_part_1_skeleton_for_students.SQL

Your company wants to merge its old product order data into a new data mart to facilitate analysis. You have been tasked with writing an ETL (extract, transform, and load) code sequence, and executing it on three years’ worth of order data.

In this assignment, you will produce SQL code which scrubs and imports each of the three years’ worth of data, and produces an output file called stagingTable.

Along with these instructions, there is another document, ‘Additional Clarification on the Week 6 Candy Assignment’. Please read that document carefully.

You should also read the ‘Data Notes’ in part 3 of this document. It is very important that you understand the data and how the data changes over the three years, so you can create a ‘stagingTable’ the effectively combines the data that might have been captured in different ways over the years.

Let’s get started!

Part A: Upload all the files you will need to SQLlite:

1. Import the file called “2017_product_data_students.csv” to SQLiteonline.com.  When you import it, give it the table name “pd2017” (no quotes) and set the column name to “First line.”

2. Import “2018_product_data_students.csv” as “pd2018”

3. Import “2019_product_data_students.csv” as “pd2019”

4. If you SELECT * FROM pd2017, you should see something like the below screenshot. Note you should see all three of the import tables on the left, and the pd2017 data should match what is shown as selected.

Part B: Extract and Transform your data

Your job is to use SQL to perform an ETL which will accomplish the following:

1. Start with the skeleton starter script we give you, attached to this assignment. Modify the CREATE TABLE command so the schema is as follows:

 

Computer   code

Notes   about what you need to do

 

DROP TABLE IF EXISTS stagingTable;

Leave   this code alone – it ensures you a fresh start

 

CREATE TABLE stagingTable (

yearInt INT(4),

monthInt INT(2),

–there will be more you need to fill in here

);

Right   now this creates stagingTable with only two fields, the yearInt and   monthInt. Modify the code where   highlighted in yellow to correspond to the schema below.

And   of course, when you’re done coding, remove the ‘– there will be more you need to fill in here’ comment J

2. Get the 2017 bit of the script working.

 

Computer   code

Notes   about what you need to do

 

–Insert 2017 Data

This   is a comment telling you the 2017 data is going to be inserted here

 

INSERT INTO   stagingTable(“monthInt”, “state”, “country”,   “region”, “Product_Name”, “unitPrice” –you need to fill this in   here)

After   you have created stagingTable, this is the first half of a command that will   insert the data into stagingTable. You   need to replace the yellow highlighted material with your own code to   complete it.

 

SELECT “Month”,   “State”, “Country”, “Region”,   “Product”, “Per-Unit_price”, “Quantity”,   “Order_Total” FROM pd2017

;

This   is the second half of a command that will insert data into stagingTable.

You   don’t need to change anything here.

 

UPDATE stagingTable SET yearInt=2017;

This   sets the year to 2017 for this data.

You   don’t need to change this.

3. Get the 2018 part of the script working.

 

Computer   code

Notes   about what you need to do

 

–Insert 2018 Data

Comment.

 

INSERT INTO stagingTable(–you need to fill this in   here)

This   is the first half of a command that will insert the data into   stagingTable. You need to replace the   yellow highlighted material with your own code to complete it. See the rules below for more details.

 

SELECT … FROM pd2018

;

This   is the second half of a command that will insert data into stagingTable.

You   need to replace the yellow highlighted material.

 

UPDATE stagingTable SET yearInt=2018 WHERE   yearInt ISNULL;

This   sets the year to 2018 for any new entries which don’t yet have a year.

You   don’t need to change this.

4. Get the 2019 part of the script working.

 

Computer   code

Notes   about what you need to do

 

–Insert 2019 Data

Comment.

 

INSERT INTO stagingTable(–you need to fill this in   here)

You   need to replace the yellow highlighted material with your own code to   complete it. See the rules below for   more details.

 

SELECT … FROM pd2019

;

You   need to replace the yellow highlighted material.

 

UPDATE stagingTable …;

You need   to replace the yellow highlighted material.

5. The script will load it into one final table and call it stagingTable

6. Run the checksum script to verify you have the stagingTable calculated correctly.

7. Export your final output table under the name “XX_output_final.csv” where XX are your initials.  To export this, you can just use the Export button on the SQLlite menu (it’s right next to the Import button.)

You should do this all in SQLlite. You should not export to Excel and do your manipulations in Excel.

Part C: 2017 Data Notes

Your order 2017 data is contained in the attached file, “2017_product_data_students.csv” and you should have imported it as “pd2017.” A sample of this file’s type of data is contained below in Table 1 Sample of order data from 2017. (Note your file may or may not have the same data in it.)

Your field definitions follow:

· Month: integer, corresponds to the month of the sale. For example, 5 = May.

· Country: text, should all be USA. (All data in this exercise should be USA.)

· Region: text, represents the regions within the country.

· State: text, USPS state abbreviations. Each state is within one region.

· Product: text. This is the name of a packaged food product.

· Per-unit price: integer. This represents the per-unit price in cents; for example, 300 indicates that Orange Creepies sell for $3.00 per package. (For the purposes of this exercise, disregard all currency formatting and just use 300 to represent $3.00.)

· Quantity: integer. This represents how many items were in that particular order. The first order here was for 49 packages of Orange Creepies.

· Order Total: integer. This is the per-unit price x the quantity. The first line here indicates that 300 x 49 = 14700 (or $147.00) was the price of the first order.

Table 1 Sample of order data from 2017

 

Month

Country

Region

State

Product

Per-Unit   Price

Quantity

Order   Total

 

0

7

USA

West

CA

Orange   Creepies

300

49

14700

 

1

9

USA

Northeast

RI

Farm   Fresh

365

49

17885

 

2

10

USA

South

TN

Farm   Fresh

365

10

3650

 

3

12

USA

South

FL

Organiks

257

27

6939

 

4

12

USA

South

MD

PearApple

363

83

30129

 

5

8

USA

South

KY

Big   Waffle

268

5

1340

2018 Data Notes:

Your order 2018 data is contained in the attached file, “2018_product_data_students.csv”

A sample of this file’s data is contained below as Table 2 Sample of order data from 2018. (Note your file may or may not have the same data in it.)

Your field definitions follow:

· Month: integer, corresponds to the month of the sale. For example, 5 = May.

· Region: text, represents the regions within the country.

· Customer_ID: integer, represents the customer’s unique Customer ID number.

· Product: text. This is the name of a packaged food product.

· Per-unit price: integer. This represents the per-unit price in cents; for example, 363 indicates that PearApple sells for $3.63  per package. (For the purposes of this exercise, you should disregard all currency formatting and just use 363 to represent $3.63.)

· Quantity_1: integer. This represents how many items were in the first shipment of that particular order. This year we had shipping problems, and could often not ship the entire order all at once. Orders were split into two shipments where necessary, and Quantity_1 reflects how many units were shipped first. (Assume all shipments were completed in the month listed, and that no shipments had the first shipment in one month and the second shipment in the subsequent month.)

· Quantity_2: integer. This represents how many items were in the second shipment of that particular order. A 0 indicates a second shipment was not necessary. To get the total number of items shipped, you need to add Quantity_1 and Quantity_2.

· The first line here reflects that PearApple has a first shipment of 25 units, and a second shipment of 92 unit, all within the month of January, for a total of 25 + 92 = 117 units.

Table 2 Sample of order data from 2018

 

Month

Region

Customer_ID

Product

Per-Unit   Price

Quantity_1

Quantity_2

 

0

1

Midwest

280

PearApple

363

25

92

 

1

5

West

545

Orange   Creepies

300

87

79

 

2

8

Northeast

131

Future   Toast

253

90

6

 

3

4

Midwest

920

Farm   Fresh

365

33

74

 

4

12

South

358

Rotpunkt

220

4

13

 

5

1

South

855

GMO   Guardian

176

17

45

2019 Data Notes:

Your order 2019 data is contained in the attached file, “2019_product_data_students.csv.”

A sample of this file’s data is contained below as Table 3 Sample of order data from 2019. (Note your file may or may not have the same data in it.)

Your field definitions follow:

· Month: integer, corresponds to the month of the sale. For example, 5 = May.

· Country: text, represents the country of the customer. Should all be USA.

· Region: text, represents the regions within the country.

· State: USPS code for the 50 United States.

· Product: text. Same as previous years.

· Per-unit price: integer. This represents the per-unit price in cents; same as previous years.

· Quantity: This represents how many items were in that particular order. The first order here was for 95 packages of Only Pancakes.

· Order Subtotal: This represents the order subtotal, calculated as per-unit price x quantity. For example, the first order here reflects a per-unit price of 413 cents x 95 units, for a subtotal of 39,235 (or $392.35).

· Quantity Discount: This represents the new policy (effective January 1, 2019) that all orders 90 units and over will automatically earn a 10% discount. An order of 89 units does not earn the discount; an order of 90 units does earn the discount. All order discounts have been rounded to the nearest penny, so you can assume this field has no decimals in it. In the data below,

o Order 0, on the first line, of 95 Only Pancakes to Florida, did qualify for the Quantity Discount, because an order quantity of 95 exceeded the 90 threshold. The Quantity Discount has been computed as 3924, or 10% of 39235. In this case, the final order total would be 39,235 – 3,924 = 35,311 (or $353.11).

o Order 4, on the fifth line, of 31 Future Toasts to North Carolina, did not qualify for the Quantity Discount. Therefore, the Order total would simply be the Order subtotal.

Table 3 Sample of order data from 2019

 

Month

Country

Region

State

Product

Per-Unit   Price

Quantity

Order   Subtotal

Quantity   Discount

 

0

9

USA

South

FL

Only   Pancakes

413

95

39235

3924

 

1

6

USA

West

HI

Big   Waffle

268

93

24924

2492

 

2

5

USA

Northeast

RI

Grey   Gummies

446

95

42370

4237

 

3

9

USA

Midwest

NE

Funky   Pops

380

100

38000

3800

 

4

11

USA

South

NC

Future   Toast

253

31

7843

0

 

5

8

USA

West

WA

Mr   Greens

447

76

33972

0

 

6

8

USA

South

MD

Giant   Gummies

347

93

32271

3227

Part D: Check Your Own Work

1.  You can run the following SQL code on your staging table. There is nothing to turn in from this bit. It should yield the following first few rows:

Select region, yearint, monthInt, count(*) from stagingTable where monthInt = 5 group by region, yearInt, monthInt;

2. You can also run the following code to debug. You should get the following rows:

Select yearInt, monthInt, state, customer_id, product_name, orderTotal from stagingTable

where product_name = ‘Big Waffle’ and monthint=4

order by product_name, yearInt, monthInt, state, customer_id, orderTotal;

Now that you’ve debugged your code, it’s time to get a checksum! Run the following code to get a checksum. The checksum will be a number. Put this checksum number on the top of your homework. See table below for help with your CHECKSUM result.

select sum(yearInt * monthInt * orderTotal)%2341 as checksum from stagingTable;

3. Once you get the result of your CHECKSUM look at table below for ways to troubleshoot any issues with your ETL statements.

 

If   you get this checksum:

Hint:

 

1021

No   further troubleshooting needed, go ahead, and submit your 3 deliverables

 

315

Look   at your calculation for OrderTotal for 2019

 

349

Look   at your calculation for OrderTotal for 2018 AND 2019

 

1944

Look   at your calculation for OrderTotal for 2018 AND 2019 (hint: subtotal   is NOT equal to ordertotal)

 

1767

Look   at how your calculation for the quantity total for 2018.

Look   at your calculation for OrderTotal for 2019

 

953

Look   at how your calculation for the quantity total for 2018.

Look   at your calculation for OrderTotal for 2018 AND 2019

 

Other

Look   carefully at your syntax for every field. Run your 2017 insert first, then   run your 2018 insert, then your 2019 insert statement.

TURN IN:

1. Your output file, called “XX_output_final.csv” where XX are your initials.

2. All the SQL code you used to execute this.

3. A document that contains

a. CHECKSUM: XXX where XXX is the checksum number produced. Put this in big font right on the top.

b. A one page outline of your ETL process. Which functions did you use, and what logic did you follow? This should be at the level that your boss, who has an MBA but not an IT/database background, can follow it. Do not use “computer-ese” here; use regular business English.