Code Explanation

The provided SQL code is a query designed to select specific columns from a dataset named Branch_Stock_Data. Below is a structured breakdown of the code to enhance understanding.

SQL Code Overview

SELECT 
    Branch_Stock_Data.[Site Id], 
    Branch_Stock_Data.[Warehouse Id], 
    Branch_Stock_Data.[Item ID], 
    Branch_Stock_Data.[Sum of Stock On Hand Qty]
FROM 
    Branch_Stock_Data
WHERE 
    (((Branch_Stock_Data.[Site Id]) Like "*P-*") 
    AND 
    ((Branch_Stock_Data.[Warehouse Id]) Like "*P-*"
    AND 
    (Branch_Stock_Data.[Warehouse Id]) Not Like "*14*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*15*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*16*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*17*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*21*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*26*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*37*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*38*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*47*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*55*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*56*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*66*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*67*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*68*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*69*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*78*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*79*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*91*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*93*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*13*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*18*"
    AND (Branch_Stock_Data.[Warehouse Id]) Not Like "*19*"));

Breakdown of Components

1. SELECT Statement

• This part of the SQL query specifies which columns to retrieve. In this case, it fetches:
  • Site Id: Unique identifier for the site.
  • Warehouse Id: Unique identifier for the warehouse.
  • Item ID: Unique identifier for the item.
  • Sum of Stock On Hand Qty: The total quantity of stock currently held.

2. FROM Clause

• Indicates the source table from which the data is selected, here it is Branch_Stock_Data.

3. WHERE Clause

• This section filters the records returned from the selected columns. It contains multiple conditions based on patterns.

3.1 Conditions Breakdown

• Site Id Condition:

  • Branch_Stock_Data.[Site Id] Like "*P-*": Filters records where the Site Id contains "P-" anywhere within it.

• Warehouse Id Conditions:

  • Two parts:
    1. Pattern Match: Branch_Stock_Data.[Warehouse Id] Like "*P-*" filters for Warehouse Ids that also include "P-".
    2. Exclusions: Several Not Like conditions exclude Warehouse Ids containing certain digit groupings (e.g., "14", "15", up to "19"). This ensures only Warehouse Ids that do not have those specified numbers will be included.

4. Logical Operators

• The AND operator is primarily used to combine multiple conditions. All specified conditions must be true for a record to be included in the final output.

Key Concepts

LIKE Operator

• This operator is used in SQL to search for a specified pattern in a column. Wildcards, such as * (which represents zero or more characters), are commonly utilized.

Using Not Like

• The Not Like condition is particularly useful to exclude records that match certain undesirable patterns or values, providing a way to refine the search results.

Additional Example

To demonstrate a simpler query achieving similar functionality, consider the following:

SELECT 
    SiteId, 
    WarehouseId, 
    ItemID, 
    SUM(StockQty) AS TotalStock
FROM 
    InventoryData
WHERE 
    SiteId LIKE '*P-*' 
    AND WarehouseId LIKE '*P-*' 
    AND WarehouseId NOT IN ('14', '15', '16', '17')
GROUP BY 
    SiteId, WarehouseId, ItemID;

• This example simplifies conditions and groups data by Site Id and Warehouse Id while summing up stock quantities.

Conclusion

The SQL query exemplifies a meticulous approach to data filtering, focusing on specific site and warehouse identifiers while excluding a broad range of undesired values. This structured way of querying is vital in data analysis for ensuring relevant and accurate results. For further learning about crafting SQL queries, consider utilizing resources available on the Enterprise DNA platform.