For The Next Five SQL Questions, Open A Notepad Session

For The Next Five Sql Questions Open A Notepad Session And Save All 5

For the next five SQL questions open a Notepad session and save all 5 answers numbered 1-5. Please paste your query for that number in and then copy the whole answer if it is short or the first few rows and the count if it’s long. This is a good example of what an ad hoc query does. German Pharmaceuticals LLC has reason to be concerned that there may be product in the marketplace that could have serious consequences if ingested so they need to know purchase history for some of their customers. You are the junior DBA so you get to create the ad hoc query needed to see if the company needs to reach out to some of their customers.

The folks in production indicate that if there has been exposure Leipzig would be the best bet. So, let’s save some lives using only our cloud based SQL compliant database and our “can do”, “roll up our sleeves” etc, etc.

Questions:

  1. Retrieve the name and city for all German customers.
  2. Pinpoint just customer(s) from Leipzig showing their customer name(s) and address.
  3. List the Customer name(s) and order date(s) for the individual(s) identified above.
  4. Show the Customer name(s), OrderID (from the Orders table), and Order Details key column for the previous query.
  5. Show the CustomerName, ContactName, Address, City, PostalCode, Country, ProductName (and let’s just hope he hasn’t eaten any of the Rà¶ssle …) integrity.

Paper For Above instruction

To address the concerns of German Pharmaceuticals LLC regarding potential product exposure in the marketplace, especially within the city of Leipzig, a series of targeted SQL queries are necessary. These queries aim to identify specific customer and order information that could help the company reach out and prevent any adverse health consequences. Below is an elaboration of each query, including the reasoning and sample SQL code, to facilitate effective data retrieval from the company's SQL-compliant database system.

1. Retrieve the name and city for all German customers

The first step involves fetching a list of all customers based in Germany, along with their respective cities. Assuming the customer information is stored in a table named Customers with columns CustomerName, Country, and City, the SQL query would be structured to filter for those with the country set as Germany.

SELECT CustomerName, City

FROM Customers


WHERE Country = 'Germany';

This query filters the customer dataset to extract only entries where the Country value matches 'Germany'. It is essential for regional targeting, especially considering the company's concern about products in the German marketplace.

2. Pinpoint just customer(s) from Leipzig showing their customer name(s) and address

To narrow down to customers specifically located in Leipzig, the query refines the previous filter, adding a condition for the City attribute. This ensures identification of customers potentially affected by exposures within Leipzig.

SELECT CustomerName, Address

FROM Customers


WHERE Country = 'Germany' AND City = 'Leipzig';

The result will include customer names and addresses for those residing in Leipzig, which is critical as the city is highlighted as the primary region of concern.

3. List the Customer name(s) and order date(s) for the individual(s) identified above

Assuming there is a related Orders table containing OrderID, CustomerID, and OrderDate, a join operation is necessary to link customers with their orders. For customers identified in Leipzig, the query retrieves their names alongside the dates they placed orders.

SELECT c.CustomerName, o.OrderDate

FROM Customers c


JOIN Orders o ON c.CustomerID = o.CustomerID


WHERE c.City = 'Leipzig' AND c.Country = 'Germany';

This query produces a list of Leipzig-based customers with their respective order dates, enabling health officials to track product exposure timelines.

4. Show the Customer name(s), OrderID (from the Orders table), and Order Details key column for the previous query

For detailed insights into specific orders linked to the Leipzig customers, an additional join with the OrderDetails table is performed. Assuming the OrderDetails table contains OrderID and a primary key such as OrderDetailID, the query fetches customer names, order IDs, and order detail identifiers.

SELECT c.CustomerName, o.OrderID, od.OrderDetailID

FROM Customers c


JOIN Orders o ON c.CustomerID = o.CustomerID


JOIN OrderDetails od ON o.OrderID = od.OrderID


WHERE c.City = 'Leipzig' AND c.Country = 'Germany';

This refined query offers granular data points, critical for traceability and health intervention planning.

5. Show the CustomerName, ContactName, Address, City, PostalCode, Country, ProductName

The final query broadens the dataset to include detailed customer contact information along with the names of products they have ordered. Assuming the relevant tables are Customers (with ContactName, Address, PostalCode) and Products (with ProductName), and that the product information is linked via OrderDetails, the SQL statement would be as follows:

SELECT c.CustomerName, c.ContactName, c.Address, c.City, c.PostalCode, c.Country, p.ProductName

FROM Customers c


JOIN OrderDetails od ON c.CustomerID = od.CustomerID


JOIN Products p ON od.ProductID = p.ProductID


WHERE c.City = 'Leipzig' AND c.Country = 'Germany';

This comprehensive query consolidates all pertinent contact and product data, essential for assessing the potential health risk and initiating customer outreach.

Conclusion

Implementing these SQL queries provides German Pharmaceuticals LLC with vital insights into their customer base within Germany, especially in Leipzig. By systematically extracting customer identities, order histories, product details, and contact information, the company can proactively identify at-risk individuals and take necessary actions to mitigate health risks. Ad hoc querying like this underscores the importance of agile data analysis in public health and consumer safety contexts.

References

  • Coronel, C., & Morris, S. (2015). Database Systems: Design, Implementation, & Management. Cengage Learning.
  • Elmasri, R., & Navathe, S. B. (2015). Fundamentals of Database Systems (7th Edition). Pearson.

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