SQL is the language used to manage, organize, and retrieve information from relational databases. Users may use a command language to choose, insert, update, and look up information in a database. SQL was developed by IBM and first appeared in the early 1970s. It is now an official standard recognized by the American National Standards Institute (ANSI). It is the most common programming language companies use; hence, understanding how to interact with the database is essential.
SQL statements allow you to enter data into a database and then modify it, delete it, search for it, or get it again. In addition, SQL may be used to keep databases running smoothly and at peak efficiency. SQL plays a crucial role in machine learning processes, especially when handling large datasets stored in relational databases.
An SQL course provides essential skills for efficiently managing, querying, and analyzing large datasets, making it a key foundation for data science, machine learning, and database management roles. Continue reading to know how.
What is SQL?
SQL mainly performs three functions:
- Define the database’s scope
- Update the database with new information
- Get information from a data source
Most relational database management systems will accept SQL as a database query language even if they have their own proprietary language.
Databases are the backbone of most interactive Web applications, and DBMSs like MySQL, PostgreSQL, and Oracle- which can all process SQL queries—power these applications. Therefore, you must take a free SQL online course to understand the query language better.
How does SQL function?
SQL is easy to use in a highly effective way. It integrates, queries, aggregates, and manipulates data to turn the massive collection of data into useful information.
Some businesses would rather not have to write their SQL for their databases and instead utilize one that comes with the database management system. For example, MySQL, which Oracle created and promoted, is a top-tier SQL database management system.
In addition, the implementation of SQL includes a server that processes queries and returns the results. Let us study the commands of SQL in the next section.
What are SQL commands?
Let’s go over the basic input commands and what they mean. This will make it easier for you to practice later.
- CREATE: It means to create or view a table in the database
- DROP: This means deleting a complete table or other objects in the database
- ALTER: It means to modify a table
- DELETE: This means deleting the record
- SELECT: Retrieve specific information from the table
- INSERT: Make a new record
- UPDATE: Modify a record
- REVOKE: Revokes permissions that users have been given
- GRANT: To give permission
These are some of the most fundamental and straightforward commands. In all versions of SQL, fundamental commands such as select, insert, update, and create function identically. An individual with a basic understanding of SQL may confidently operate in various settings and carry out diverse duties.
The SQL programming language has the following main elements: keywords, queries, expressions, predicates, clauses, etc.
Data analysts and specialists can classify SQL commands under the following categories:
- Definition of Data Language
- Data Query Language
- Data Manipulation Language
- Data Control Language
- Transaction Control Language
SQL for Machine Learning
1. Data Extraction
Machine learning begins with gathering and preparing data. SQL is essential for:
- Selecting relevant data:
SELECT * FROM customers WHERE purchase_amount > 100;
- Joining tables to aggregate data:
SELECT customers.name, orders.total
FROM customers
JOIN orders ON customers.id = orders.customer_id;
2. Data Cleaning and Preprocessing
Data needs to be cleaned before it can be fed into ML models, and SQL is helpful in tasks like:
- Handling missing values:
SELECT * FROM customers WHERE age IS NOT NULL;
- Removing duplicates:
DELETE FROM customers WHERE id IN (SELECT id FROM customers GROUP BY email HAVING COUNT(*) > 1);
3. Data Transformation
Transforming raw data into useful formats:
- Normalization: Scaling values between 0 and 1:
SELECT (value – MIN(value)) / (MAX(value) – MIN(value)) AS normalized_value
FROM sales_data;
- Creating new features:
SELECT *, (price * quantity) AS total_sales FROM transactions;
4. Feature Engineering
Generating new features that improve model performance:
- Aggregating data (e.g., average, sum):
SELECT customer_id, AVG(purchase_amount) AS avg_purchase
FROM transactions
GROUP BY customer_id;
5. Model Training (In-database ML)
Some databases (e.g., BigQuery ML, PostgreSQL, SQL Server ML) allow direct model training using SQL. Example for BigQuery ML:
CREATE OR REPLACE MODEL `my_project.my_dataset.model`
OPTIONS(model_type=’linear_reg’, input_label_cols=[‘target’]) AS
SELECT * FROM my_dataset.training_data;
6. Model Inference
After training, you can use SQL to make predictions directly from the database:
SELECT predicted_label, actual_label
FROM ML.PREDICT(MODEL `my_project.my_dataset.model`, (
SELECT * FROM my_dataset.test_data
));
7. Post-processing Predictions
SQL is also used for evaluating and storing model results:
Calculate accuracy or error rates:
SELECT AVG(ABS(predicted_value – actual_value)) AS mean_absolute_error
FROM predictions;
8. Performance Tuning
Optimizing queries and data retrieval is important for handling large datasets used in ML:
Indexing:
CREATE INDEX idx_customer ON customers(customer_id);
Partitioning large tables for faster access:
CREATE TABLE partitioned_table
PARTITION BY RANGE (date)
AS SELECT * FROM original_table;
Use Cases of SQL in ML:
- Customer segmentation: SQL helps retrieve and group customers based on behavior for targeted marketing.
- Anomaly detection: SQL can identify unusual patterns in transactions or logs.
- Predictive modeling: Using SQL extensions like BigQuery ML for predicting churn, sales forecasts, etc.
SQL’s power in handling large-scale data makes it an essential tool for preparing data, training models in databases, and making real-time predictions.
Benefits of using SQL
- No coding skills required
Coding is tough to learn and requires a lot of practice and skills. But SQL is not a complex programming language; free online training will teach you how to give basic commands like create, insert, and delete.
- Interactive language
Once you understand SQL completely, it is highly interactive. Adopting it in a business environment is intended to make communications clear and productive, so there are no misunderstandings or miscommunications.
- Queries are processed quickly
SQL can quickly and efficiently retrieve large amounts of data. Thus, it allows you to be productive in other departments needing attention and provides accurate data.
- Standardized language
It mainly uses easy English terminology and sentence structure. This makes it highly accessible, as English is an elementary language to read and write, almost like a universally accepted language.
- Portability
SQL is portable and can be used on laptops, servers, and PCs. It is also compatible with intranets and the local internet. Finally, since it can be easily moved from one device to another, it’s a practical choice for users.
- Availability
The majority of popular database management systems use SQL, including the industry-standard IBM DB2, Oracle, and Microsoft SQL Server. The fact that anybody can access SQL is a major plus.
- Open source code
SQL databases may be accessed using open-source solutions like MySQL, MariaDB, and Postgres SQL.
Conclusion
SQL remains an invaluable tool in the machine learning workflow, from data extraction and cleaning to feature engineering and model deployment. By integrating SQL with machine learning processes, organizations can streamline data workflows, improve model accuracy, and make data-driven decisions faster. As machine learning evolves, SQL will remain a fundamental skill for data scientists and engineers.