It 330 Lab 5 SQL Objectives 1 Explore More Advanced SQL Quer

It 330 Lab 5 Sqlobjectives1 Explore More Advanced Sql Queries2 An

It 330 Lab 5: SQL Objectives: 1. Explore more advanced SQL queries. 2. Answer the questions, copy the SQL code, create screenshots, and submit the results. Submission requirements: · For all text and image submissions, use MS Word, which is available to you within the Virtual Desktop Infrastructure (VDI). · For all SQL code submissions, use MS Word, which is available to you within VDI. · For all diagram submissions, use MS Visio, which is available to you within VDI. · Note: If you need assistance on how to get started with this tool, go to the references section at the end of this document. · If the submission is more than one file: 1. Name each item appropriately. a. For example: LAB5-SQL-yourName.vsd, LAB5-Questions-yourName.docx 2. Save each item in a single folder. 3. This folder should also be named appropriately. a. For example: LAB5-yourName 4. Compress the folder. 5. Submit the compressed file in Blackboard. Lab: 1. Using the AdventureWorks database, query “Sales.SalesOrderDetail” table to find out the total number of parts associated with each of the following SalesOrderIDs: 43660, 43670, and 43672. This task should be completed in a single query. Submit the SQL statement used to accomplish this task. · How many rows were affected? · Provide a screenshot of the result set. 2. Query “Sales.Store” table to obtain the list of “Name” and “SalesPersonID” where the store name starts with the letters “g” through “j” and the SalesPersonID is greater than 283. Make sure that the list is sorted by “SalesPersonID” and then by “Name” fields in a descending order. Be sure the query returns all the required rows. a. Submit the SQL statement used to accomplish this task. b. How many rows were affected? c. Provide a screenshot of the result set. Hint: Combine WHERE clause conditions and also have an ORDER BY clause in place.

Paper For Above instruction

It 330 Lab 5 Sqlobjectives1 Explore More Advanced Sql Queries2 An

Exploring Advanced SQL Queries Using AdventureWorks Database

In modern database management and data analysis, proficiency with advanced SQL queries is essential for extracting meaningful insights from large datasets. The objectives of this lab focus on honing skills in writing complex SQL statements, particularly using the AdventureWorks database, a widely used sample database for learning SQL. This paper discusses two key tasks: calculating total parts for specific sales orders and retrieving filtered, sorted store details. Detailed explanations of each task, the SQL code involved, and considerations for accurate results are included.

Task 1: Calculating Total Parts for Specific Sales Orders

The first task involves querying the “Sales.SalesOrderDetail” table to determine how many parts are associated with specific SalesOrderIDs: 43660, 43670, and 43672. This requires aggregating data across multiple sales order entries within a single query, emphasizing the use of conditional filtering and aggregation functions such as COUNT or SUM.

The SQL statement employed for this task must efficiently filter for the specified IDs and count the number of parts per sales order. An example SQL query might look like:

SELECT SalesOrderID, COUNT(*) AS TotalParts

FROM Sales.SalesOrderDetail


WHERE SalesOrderID IN (43660, 43670, 43672)


GROUP BY SalesOrderID;

This query produces a result set showing each specified SalesOrderID alongside the total number of parts associated with it. The number of affected rows would be equal to the number of SalesOrderIDs queried, typically three in this case. A screenshot of the result set should display the rows with IDs and their corresponding count, validating the query's accuracy.

Task 2: Filtering and Sorting Store Data

The second task requires retrieving store names and their associated sales representatives’ IDs from the “Sales.Store” table. The filters specify store names starting with any letter from "g" through "j" and having a SalesPersonID greater than 283. These conditions necessitate a combined WHERE clause with string pattern matching and numeric filtering.

Additionally, to meet the sorting criteria, the results should be ordered by “SalesPersonID” and “Name” in descending order. An example SQL query for this task is:

SELECT Name, SalesPersonID

FROM Sales.Store


WHERE Name LIKE 'g%' OR Name LIKE 'h%' OR Name LIKE 'i%' OR Name LIKE 'j%'


AND SalesPersonID > 283


ORDER BY SalesPersonID DESC, Name DESC;

Alternatively, a more concise approach uses the BETWEEN clause for alphabet ranges or a CASE statement, enhancing readability:

SELECT Name, SalesPersonID

FROM Sales.Store


WHERE Name >= 'g' AND Name 

AND SalesPersonID > 283


ORDER BY SalesPersonID DESC, Name DESC;

This query ensures all matching rows are retrieved, sorted correctly, and presented comprehensively. The number of affected rows depends on the data, but the query guarantees all valid entries are included. A screenshot of the output should reflect the desired filtering and sorting.

Conclusion

Mastering advanced SQL queries through tasks such as aggregating sales order details and filtering store records prepares students and practitioners for real-world data challenges. Accurate filtering, appropriate aggregation, and precise sorting are fundamental skills. Leveraging sample databases like AdventureWorks enhances understanding of complex query structures, enabling effective data extraction and analysis.

References

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