Data cleansing, also known as data cleaning or data scrubbing, is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in data preprocessing and analysis to ensure the quality and reliability of data.
One common technique used in data cleansing is the use of JOIN operations in SQL. JOIN allows us to combine data from multiple tables based on a related column, enabling us to eliminate redundant or inconsistent records. In this blog post, we will explore how to use JOIN for data cleansing.
Table Structure
Before we dive into the JOIN operations, let’s define our tables’ structure for this example:
Table: Customers
| customer_id | name | address | |
|---|---|---|---|
| 1 | John | john@test.com | 123 Main St |
| 2 | Emma | emma@test.com | 456 Maple Ave |
| 3 | David | david@test.com | 789 Oak Rd |
| 4 | Sarah | sarah@test.com | 789 Oak Rd |
Table: Orders
| order_id | customer_id | product | quantity |
|---|---|---|---|
| 1 | 1 | Apple | 5 |
| 2 | 2 | Orange | 2 |
| 3 | 3 | Banana | 3 |
| 4 | 4 | Apple | 1 |
Inner Join
An inner join returns only the matching records from both tables based on the specified condition. In our example, we can use an inner join to identify invalid records in the Customers table by joining it with the Orders table:
SELECT *
FROM Customers
INNER JOIN Orders
ON Customers.customer_id = Orders.customer_id;
The result will be:
| customer_id | name | address | order_id | product | quantity | |
|---|---|---|---|---|---|---|
| 1 | John | john@test.com | 123 Main St | 1 | Apple | 5 |
| 2 | Emma | emma@test.com | 456 Maple Ave | 2 | Orange | 2 |
| 3 | David | david@test.com | 789 Oak Rd | 3 | Banana | 3 |
| 4 | Sarah | sarah@test.com | 789 Oak Rd | 4 | Apple | 1 |
Based on the inner join result, we can see that all the records in the Customers table have valid corresponding records in the Orders table.
Left Join
A left join returns all records from the left (first) table and the matching records from the right (second) table. If there is no match, NULL values are returned for the right table. We can use a left join to identify missing or orphaned records in the Orders table:
SELECT *
FROM Orders
LEFT JOIN Customers
ON Orders.customer_id = Customers.customer_id
WHERE Customers.customer_id IS NULL;
The result will be:
| order_id | customer_id | product | quantity | name | address | |
|---|---|---|---|---|---|---|
| 5 | NULL | Watermelon | 1 | NULL | NULL | NULL |
In this example, there is a record in the Orders table with a customer_id value that does not exist in the Customers table. This could be an indication of a data issue or a record that needs further investigation.
Conclusion
JOIN operations are powerful tools for data cleansing by allowing us to cross-reference data from multiple tables. The inner join helps us identify and validate records, while the left join helps us uncover missing or orphaned records.
By utilizing JOIN operations effectively, we can ensure the accuracy and completeness of our datasets, leading to more reliable and insightful analysis.
#datacleansing #SQL