Data Structuring: Joining and Splitting Data

Often, the data you extract isn't perfectly organized in one place. Information about a single entity, like a customer, might exist partially in one dataset (e.g., basic profile) and partially in another (e.g., their order history). Similarly, a single dataset might contain too much varied information, making it unwieldy. Data structuring techniques, specifically joining and splitting, help reshape your data during the transformation stage to create more logical and useful structures for analysis or loading into the target system.

Combining Related Data: Joins

Joining is the process of combining rows from two or more tables or data streams based on a related column between them. Think of it like matching puzzle pieces. If you have a list of customers and a separate list of orders, you can "join" them using a common identifier, like a CustomerID, to see which customer placed which order.

The column used for matching is often referred to as a join key. For joins to work effectively, these keys need to exist in the datasets you want to combine. Common examples include unique IDs (like UserID, ProductID, OrderID) or sometimes combinations of fields.

There are several ways to join data, depending on what information you want to keep:

1. Inner Join: This is the most common type. An inner join returns only the rows where the join key exists in both tables. If a customer exists in the customer list but has never placed an order, they won't appear in the result of an inner join between customers and orders.

   An inner join combines customers and orders based on matching CustomerID. Charlie (ID 3) is excluded as they have no orders.

2. Left Join (or Left Outer Join): This join returns all rows from the "left" table (the first table mentioned in the join operation) and the matched rows from the "right" table (the second table). If there's no match for a row from the left table in the right table, the columns from the right table will contain NULL or empty values for that row. This is useful when you want to keep all records from one primary table and add related information where available.

   A left join keeps all customers (left table) and adds order details where CustomerID matches. Charlie (ID 3) is included, but OrderID and Product are NULL because they have no matching orders.

3. Right Join (or Right Outer Join): This is the mirror image of a left join. It returns all rows from the "right" table and matched rows from the "left" table. Rows in the right table with no match in the left table will have NULL values for the left table's columns.

4. Full Outer Join: This join returns all rows when there is a match in either the left or the right table. It essentially combines the results of a Left Join and a Right Join. If a row from one table doesn't have a match in the other, the columns from the non-matching table will be filled with NULL values.

Choosing the right join type depends entirely on the question you're trying to answer or the structure required by your target system.

Dividing Data: Splitting

Splitting is the process of breaking down a single large table or dataset into two or more smaller, more focused tables. This is often done for reasons of organization, efficiency, or to conform to a specific database design principle called normalization.

Normalization aims to reduce data redundancy (avoiding storing the same piece of information multiple times) and improve data integrity.

Here are common scenarios where splitting is useful:

1. Separating Repeated Groups: Imagine a dataset where each row contains an order ID, customer details (name, address, email), and product details (product ID, name, price). If a customer places multiple orders, their name, address, and email are repeated in each row. Splitting this into an Orders table (OrderID, CustomerID, OrderDate) and a Customers table (CustomerID, Name, Address, Email) eliminates this repetition. The tables can be linked using CustomerID.

2. Decomposing Complex Fields: Sometimes a single column contains multiple pieces of information. For example, a single Address column might contain "123 Main St, Anytown, CA 94000". Splitting this into separate StreetAddress, City, State, and ZipCode columns makes the data much easier to query and analyze (e.g., finding all customers in a specific state).

3. Isolating Different Concepts: A wide table might mix information about different entities. For instance, a product table might include supplier information (supplier name, contact). Splitting this into a Products table and a Suppliers table, linked by a SupplierID, makes the data model cleaner and easier to understand. Each table focuses on a single subject.

Example: Splitting Customer and Address Data

Let's say you start with data like this:

Initial Data:

Notice Alice's address is repeated. We can split this:

Customers Table (After Split):

Addresses Table (After Split):

Now, the address "10 Pine St, Metro, NY" is stored only once in the Addresses table, identified by AddressID 101. The Customers table uses this AddressID to link customers to their address, reducing redundancy. If Alice's address changes, you only need to update it in one place.

Both joining and splitting are fundamental transformation techniques used to reshape data into a format that is clean, efficient, and suitable for loading into the target system or for subsequent analysis steps. They allow you to combine disparate information sources or break down complex datasets into more manageable and logical components.