When working with large datasets, it is common to encounter different data types, such as numerical values, strings, dates, or booleans. To perform efficient filtering and aggregation operations on these varied data types, it is essential to leverage advanced techniques provided by SQL. In this article, we will explore some of these techniques to enhance your data manipulation capabilities.

1. Filtering Numerical Data

In SQL, you can filter numerical data using comparison operators, such as =, <>, <, >, <=, or >=. For example, to retrieve all the records with sales greater than 1000, you can write:

SELECT *
FROM sales
WHERE sales > 1000;

Furthermore, you can combine multiple conditions using logical operators (AND, OR) to create complex filtering criteria.

2. Filtering String Data

When dealing with string data, you often need to search for specific patterns or values within a string column. SQL provides the LIKE operator along with wildcard characters to accomplish this. The wildcard characters include % (matches any sequence of characters) and _ (matches any single character). For instance, to retrieve all products starting with “A”, you can use:

SELECT *
FROM products
WHERE product_name LIKE 'A%';

Additionally, you can use the IN operator to filter data based on a specific set of values. For example, to retrieve records with product_category either “Electronics” or “Appliances”, you can write:

SELECT *
FROM products
WHERE product_category IN ('Electronics', 'Appliances');

3. Filtering Date and Time Data

SQL offers functions and operators to filter data based on date and time values. For instance, to retrieve all records before a specific date, you can use the < operator with the DATE function. To retrieve all sales made in the current year, you can utilize the YEAR function. Consider the following examples:

SELECT *
FROM sales
WHERE sale_date < DATE '2022-01-01';

SELECT *
FROM sales
WHERE YEAR(sale_date) = YEAR(CURRENT_DATE);

4. Aggregating Data

Aggregating data is a fundamental operation in SQL, allowing you to summarize and analyze large datasets. Some commonly used aggregation functions include COUNT, SUM, AVG, MIN, and MAX. Here are some examples:

SELECT COUNT(*)
FROM sales;

SELECT SUM(sales_amount)
FROM sales;

SELECT AVG(unit_price)
FROM products;

SELECT MIN(order_date)
FROM orders;

SELECT MAX(quantity)
FROM order_items;

Conclusion

With advanced filtering and aggregation techniques in SQL, you can efficiently manipulate and analyze data based on different data types. By utilizing these techniques effectively, you can streamline your data operations and gain valuable insights from your datasets.

#SQL #DataManipulation