Introduction

Imagine having access to an enormous pool of data, but lacking the tools to retrieve or manage it effectively. In the realms of Artificial Intelligence (AI) and Machine Learning (ML), obtaining the right data is a critical foundation for building successful models. Among the many tools and libraries used for data acquisition, Structured Query Language (SQL) remains one of the most powerful and widely adopted. As the backbone of data retrieval and management in relational databases, SQL plays a vital role across industries in handling structured data efficiently.

By the end of this reading, you will be able to: 

• Summarize the critical components of SQL that make it such an essential tool for data acquisition.

1. Overview of SQL

What is SQL? SQL is a standardized language used to communicate with relational databases. It allows you to perform a variety of operations on the data stored within these databases, such as querying, updating, inserting, and deleting records. SQL is essential for extracting meaningful information from large datasets, making it a cornerstone of data acquisition in data science and ML projects.

2. Key components of SQL

To effectively use SQL for data acquisition, it’s important to understand its core components. Let’s break down some of the most critical SQL operations and commands that you’ll encounter:

SELECT statement

Definition: The SELECT statement is the most fundamental operation in SQL. It’s used to query the database and retrieve specific data based on defined criteria.

Example:

SELECT first_name, last_name, email
FROM customers
WHERE country = 'USA';

Discussion: In this example, the SELECT statement retrieves the first_name, last_name, and email columns from the customers table where the country is ‘USA’ The SELECT statement is versatile and can be combined with other SQL clauses to filter, sort, and aggregate data, making it a powerful tool for data acquisition.

WHERE clause

Definition: The WHERE clause is used to filter records based on specific conditions. It helps narrow down the data returned by a query.

Example:

SELECT * FROM orders
WHERE order_date > '2025-01-01';

Discussion: Here, the WHERE clause filters the orders table to return only those records where the order_date is after January 1, 2025. The ability to filter data based on the conditions is essential for acquiring relevant subsets of data for analysis or model training.

JOIN operations

Definition: The JOIN operation is used to combine rows from two or more tables based on a related column between them. It’s crucial for working with normalized databases where related data is stored in separate tables.

Types of JOINs:

• INNER JOIN: This returns only the records that have matching values in both tables.
• LEFT JOIN (or LEFT OUTER JOIN): This returns all records from the left table and the matched records from the right table. If no match is found, NULL values are returned for columns from the right table.
• RIGHT JOIN (or RIGHT OUTER JOIN): This returns all records from the right table and the matched records from the left table.
• FULL JOIN (or FULL OUTER JOIN): This returns all records when there is a match in either the left or right table.

Example:

SELECT customers.first_name, customers.last_name, orders.order_id
FROM customers
INNER JOIN orders ON customers.customer_id = orders.customer_id;

Discussion: This query uses an INNER JOIN to combine data from the customers and orders tables, matching records based on the customer_id column. The result is a list of customers and their associated order IDs. JOIN operations are fundamental for combining related data across different tables, allowing for comprehensive data retrieval.

GROUP BY and aggregation functions

Definition: The GROUP BY clause groups rows that share the same values in specified columns into summary rows, often used with aggregation functions, such as COUNT(), SUM(), AVG(), MIN(), and MAX().

Example:

SELECT product_id, COUNT(*) AS total_sales
FROM sales
GROUP BY product_id;

Discussion: In this query, the GROUP BY clause groups the data by product_id and uses the COUNT() function to calculate the total number of sales for each product. Aggregation functions are essential for summarizing large datasets, making them easier to analyze and interpret.

ORDER BY clause

Definition: The ORDER BY clause is used to sort the result set of a query by one or more columns, either in ascending (ASC) or descending (DESC) order.

Example:

SELECT first_name, last_name, email
FROM customers
ORDER BY last_name ASC;

• Discussion: This query sorts the results by the last_name column in ascending order. The ORDER BY clause is useful for organizing data in a way that makes it easier to analyze, especially when working with large datasets.

INSERT, UPDATE, and DELETE statements

INSERT: adds new records to a table

Example:

INSERT INTO customers (first_name, last_name, email)
VALUES ('Mike', 'r', 'mike.r@make.com');

UPDATE: modifies existing records in a table

Example:

UPDATE customers
SET email = 'new.email@make.com'
WHERE customer_id = 23;

DELETE: removes records from a table

Example:

DELETE FROM customers
WHERE customer_id = 23;

Discussion: These commands allow you to manage the data within your database, adding new information, updating existing records, or removing data that is no longer needed. Proper use of these commands is critical for maintaining the integrity and accuracy of your datasets.

3. Advanced SQL concepts

While the basics of SQL cover most day-to-day data acquisition needs, there are more advanced SQL concepts that can be particularly useful in complex data environments:

Subqueries and nested queries

Definition: A subquery is a query within another query. It allows for more complex data retrieval operations.

Example:

SELECT first_name, last_name
FROM customers
WHERE customer_id IN (SELECT customer_id FROM orders WHERE order_date > '2025-01-01');

Discussion: This query retrieves the names of customers who have placed orders after January 1, 2025. The subquery within the IN clause first selects the relevant customer_ids from the orders table. Subqueries are powerful for performing operations that require multiple steps or complex conditions.

Indexing

Definition: Indexes are used to speed up the retrieval of rows by using a pointer. Creating an index on a column means the database will keep a sorted copy of that column, allowing for faster search and query operations.

Example:

CREATE INDEX idx_customer_id ON customers (customer_id);

Discussion: Indexing improves the performance of queries, especially on large datasets. However, it’s important to use indexing judiciously, as it can also increase the time it takes to write new data to the database.

Transactions and ACID properties

Definition: A transaction is a sequence of one or more SQL operations treated as a single unit. The ACID properties—atomicity, consistency, isolation, and durability—ensure that the transactions are processed reliably.

Example:

BEGIN TRANSACTION;
UPDATE accounts SET balance = balance - 100 WHERE account_id = 1;
UPDATE accounts SET balance = balance + 100 WHERE account_id = 2;
COMMIT;

Discussion: In this example, money is transferred between two accounts. The transaction ensures that both updates occur, or neither does, maintaining data integrity. Understanding transactions is crucial when working with complex operations that must remain consistent, even in the event of an error.

Conclusion

SQL is an indispensable tool for data acquisition, especially when working with structured data in relational databases. By mastering the key components of SQL—such as SELECT statements, WHERE clauses, and JOIN operations—you can efficiently retrieve and manage the data necessary for training and deploying AI/ML models. 

As you continue to build your skills, these SQL commands and concepts will form the foundation of your data acquisition strategy, enabling you to work with large and complex datasets effectively.