This assignment will test your understanding of conditional logic, views, ranking and windowing functions, and transactions
This assignment will test your understanding of conditional logic, views, ranking and windowing functions, and transactions, as shown in the videos. The prompt includes 10 questions. You will need to create your own test database and tables using the criteria below. Please submit your answers using only one file. The preferable format is a text file with a .sql extension.
You can easily edit the file using a text editor such as Notepad ++, which is available online for free. Prompt : A manufacturing company’s data warehouse contains the following tables. Region region_id (p) region_name super_region_id (f) 101 North America 102 USA Canada USA-Northeast USA-Southeast USA-West Mexico 101 Note: (p) = "primary key" and (f) = "foreign key". They are not part of the column names. Product product_id (p) product_name 1256 Gear - Large 4437 Gear - Small 5567 Crankshaft 7684
Sprocket Sales_Totals product_id (p)(f) region_id (p)(f) year (p) month (p) sales Answer the following questions using the above tables/data: Write a CASE expression that can be used to return the quarter number (1, 2, 3, or 4) only based on the month.
Paper For Above instruction
The following comprehensive SQL solutions address each of the ten questions based on the provided manufacturing data warehouse schema. The queries demonstrate fundamental and advanced SQL techniques, including conditional logic, pivoting, ranking, transactions, view creation, and joins, necessary for robust data analysis in a warehousing environment.
1. Generating Quarter Number Using a CASE Expression
To determine the quarter based on the month, a simple CASE
expression is used. The logic maps months 1-3 to Q1, 4-6 to Q2, 7-9 to Q3, and 10-12 to Q4.
SELECT month, CASE
WHEN month BETWEEN 1 AND 3 THEN 1
WHEN month BETWEEN 4 AND 6 THEN 2
WHEN month BETWEEN 7 AND 9 THEN 3
WHEN month BETWEEN 10 AND 12 THEN 4
ELSE NULL
END AS quarter_number FROM Sales_Totals;
2. Pivoting Sales Data for 2020 by Product
Pivoting involves aggregating sales across months for each product. MySQL lacks a PIVOT function, but conditional aggregation with SUM and CASE can achieve this. Assuming the product_id values for the products and a fixed year 2020:
SELECT
SUM(CASE WHEN product_id = 1256 AND year = 2020 THEN sales ELSE 0 END) AS tot_sales_large_gear,
SUM(CASE WHEN product_id = 4437 AND year = 2020 THEN sales ELSE 0 END) AS tot_sales_small_gear,
SUM(CASE WHEN product_id = 5567 AND year = 2020 THEN sales ELSE 0 END) AS tot_sales_crankshaft,
SUM(CASE WHEN product_id = 7684 AND year = 2020 THEN sales ELSE 0 END) AS tot_sales_sprockets
FROM Sales_Totals
WHERE year = 2020;
3. Calculating Overall Sales Rank
This queries assigns a rank to each row based on total sales across all data, in descending order, using RANK() or DENSE_RANK() window function.
SELECT st.*,
RANK() OVER (ORDER BY st.sales DESC) AS sales_rank
FROM Sales_Totals st;
4. Product-wise Sales Ranking with PARTITION BY
This assigns ranking within each product, ordered by sales descending.
SELECT st.*,
RANK() OVER (PARTITION BY st.product_id ORDER BY st.sales DESC) AS product_sales_rank
FROM Sales_Totals st;
5. Filtering for Top 2 Sales Per Product
This query filters rows where the rank within each product is 1 or 2, using a subquery or CTE: WITH RankedSales AS ( SELECT st.*,
RANK() OVER (PARTITION BY st.product_id ORDER BY st.sales DESC) AS product_sales_rank
FROM Sales_Totals st ) SELECT * FROM RankedSales
WHERE product_sales_rank <= 2;
6. Transaction: Adding Region and Sales Data
This involves inserting a new region ('Europe') and a corresponding sales record for Sprocket in October 2020 with sales of $1,500, wrapped in a transaction: START TRANSACTION;
INSERT INTO Region (region_id, region_name, super_region_id)
VALUES (103, 'Europe', 103); -- Assuming new region_id 103
INSERT INTO Sales_Totals (product_id, region_id, year, month, sales)
VALUES (7684, 103, 2020, 10, 1500);
COMMIT;
7. Creating a View with Grouped Sales Data
The view groups sales by product and year, summing sales for each. Additionally, gear products are identified via CASE expressions based on product_id.
CREATE VIEW Product_Sales_Totals AS SELECT product_id, year, SUM(sales) AS product_sales, SUM(CASE
WHEN product_id IN (1256, 4437) THEN sales
ELSE 0
END) AS gear_sales
FROM Sales_Totals
GROUP BY product_id, year;
8. Calculating Percentage of Sales per Product for 2020
Here, total sales for 2020 are calculated, then each row's percentage contribution is determined. WITH TotalSales2020 AS ( SELECT SUM(sales) AS total_sales FROM Sales_Totals
WHERE year = 2020 ) SELECT
st.product_id, st.region_id, st.month, st.sales, (st.sales / ts.total_sales) * 100 AS pct_product_sales
FROM Sales_Totals st, TotalSales2020 ts WHERE st.year = 2020;
9. Computing Prior Month Sales
This uses window functions to retrieve previous month's sales data, assuming the data covers only 12 months for 2020:
SELECT year, month, sales,
LAG(sales) OVER (ORDER BY year, month) AS prior_month_sales
FROM Sales_Totals
WHERE year = 2020;
10. Data Dictionary for Product Table in 'sales' Database
Retrieving column names and data types is achieved via the information_schema.columns
table:
SELECT column_name, data_type FROM information_schema.columns
WHERE table_schema = 'sales' AND table_name = 'Product';
References
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