How to Gain SQL Experience: A Step-by-Step Guide for Beginners

Many beginner data enthusiasts struggle to grasp the fundamentals of relational databases and SQL despite their critical importance in the data field. With relational databases accounting for over 72% of the database market share in 2024, their dominance is evident.

Since SQL is the most widely used language in the data-related field, it’s inherently tied to relational databases as the primary tool for querying and managing data. Consequently, mastering SQL is essential for data professionals seeking to excel in analytics, business intelligence, and database management.

SQL is among the skills learned in a minute but takes years to master. While pretty skilled in SQL queries, a significant percentage of professionals in the data field fail to summon their knowledge regarding practical applications.

To help you avoid the disparity, we’ve crafted this step-by-step guide to strengthen your technical skills and prepare you for tackling practical challenges in the industry with confidence.

While lacking fundamental SQL skills may not directly prevent you from gaining SQL experience, it could hinder your progress. Here are some key SQL concepts that our data scientist candidates frequently rely on in their respective fields:

Database Concepts

Let’s discuss some of the database concepts you need to understand before starting to experience SQL complexities:

• Relational Databases: Understand the structure of relational databases, where data is organized into tables consisting of rows (records) and columns (attributes).
• Primary Keys: Learn unique identifiers for rows, ensuring no duplicates.
• Foreign Keys: Establish relationships between tables, enabling data linking and integrity.
• Normalization: Practice structuring data to eliminate redundancy and ensure consistency.

Basic Querying

Basic SQL querying includes:

• SELECT: Retrieve specific columns and rows from a table.

Example:

SELECT name, age FROM users;

• WHERE: Filter data based on specific conditions.

Example:

SELECT * FROM users WHERE age > 30;

• ORDER BY: Sort results in ascending or descending order.

Example:

SELECT * FROM users ORDER BY age DESC;

• LIMIT: Restrict the number of results returned.

Example:

SELECT * FROM users LIMIT 10;

Data Manipulation

SQL allows for changing the data within a table:

• INSERT: Adds new rows.

Example:

INSERT INTO employees (name, age) VALUES ('Alice', 30);.

• UPDATE: Modifies existing records.

Example:

UPDATE employees SET age = 31 WHERE name = 'Alice';.

• DELETE: Removes rows.

Example:

DELETE FROM employees WHERE age < 25;.

Data Definition

Define or alter the structure of tables and databases:

• CREATE: Used to set up new tables.

Example:

CREATE TABLE employees (id INT, name VARCHAR(50));.

• ALTER: Modify a table’s structure, like adding a new column.

Example:

ALTER TABLE employees ADD email VARCHAR(100);.

• DROP: Permanently delete tables or databases.

Example:

DROP TABLE employees;.

Joining Tables

Combine data from multiple tables to gain comprehensive insights:

• INNER JOIN: Retrieves matching rows from both tables.
• LEFT JOIN: Includes all rows from the left table and matching rows from the right table.
• RIGHT JOIN: Similar to LEFT JOIN, but includes all rows from the right table.
• FULL OUTER JOIN: Combines all rows from both tables, including unmatched ones.

Aggregation and Grouping

• GROUP BY: Groups data into categories, such as:

SELECT department, COUNT(*) FROM employees GROUP BY department;.

• Aggregate Functions: Use functions like COUNT, AVG, SUM, MAX, and MIN to analyze grouped data.

Working with Constraints

SQL constraints ensure data integrity and security:

• NOT NULL: Prevents null values in specified columns.
• UNIQUE: Ensures no duplicate values in a column.
• DEFAULT: Sets a default value for a column when no value is provided.

Indexing and Query Optimization

You may optimize query performance by understanding indexing:

• Indexes: Speed up search operations on large datasets by indexing frequently queried columns.
• Query Optimization: Write efficient queries to minimize execution time, such as avoiding SELECT *.

SQL Functions

Use built-in SQL functions to process and analyze data:

• String Functions: Modify text, such as CONCAT(first_name, ' ', last_name);.
• Date Functions: Handle dates, e.g., NOW() retrieves the current timestamp.
• Mathematical Functions: Perform calculations like ROUND(price, 2);.

Permissions

Manage database security and user access:

• GRANT: Assign permissions to users, like GRANT SELECT ON employees TO user1;.
• REVOKE: Remove permissions, e.g., REVOKE SELECT ON employees FROM user1;. Properly managing permissions ensures sensitive data remains secure.

Setting up your SQL environment depends upon your choice of DBMS. Decide which DBMS you want to work with. The popular options include:

• MySQL: Open-source and widely used.
• PostgreSQL: Advanced, open-source DBMS with rich features.
• SQLite: Lightweight and great for beginners.
• Microsoft SQL Server: Common in enterprise environments.
• Oracle Database: Robust and commonly used in large enterprises.

Being most widely used, we’ll talk about MySQL in this section (and SQLite in the next section), but you may choose anything and follow their documentation on their respective official websites.

To set up MySQL, follow this detailed step-by-step process:

Step 1: Download MySQL

1. Visit the official MySQL Community Downloads page.
2. Choose the appropriate installer for your operating system (Windows, macOS, or Linux).
   • For Windows, the MySQL Installer is recommended.
   • For macOS or Linux, you may also use package managers (e.g., brew install mysql for macOS or apt-get install mysql-server for Linux).

Step 2: Install MySQL

1. Run the Installer: Open the downloaded file and follow the installation prompts.
2. Choose Installation Type:
   • Developer Default: Includes MySQL Server, Workbench, and other tools.
   • Custom: Lets you choose specific components.
3. Configure MySQL Server:
   • Set a root password for administrative access. Save this password securely.
   • Select default server settings or customize based on your environment (e.g., development or production).
4. Complete Installation: Finalize the setup and close the installer once complete.

Step 3: Start MySQL Server

• Windows: MySQL Server usually starts automatically. You can manage it via the Services app or the MySQL Notifier.

• macOS/Linux: Start the server manually using:

  sudo service mysql start
  

  Or check its status with:

  sudo service mysql status
  

Step 4: Verify Installation

1. Open a terminal (or command prompt) and run:

   mysql --version
   

   This command should return the installed version of MySQL. 2. Log in to the server:

   mysql -u root -p
   

   Enter the root password you set during installation. Successful login confirms your installation.

Step 5: Install MySQL Workbench (Optional)

• MySQL Workbench provides a user-friendly GUI for managing databases.
• Download it from the MySQL Workbench download page.
• Install and launch it, then connect to your MySQL Server using the root credentials.

Step 6: Create Your First Database

1. Using the Command Line:

   CREATE DATABASE my_first_database;
   USE my_first_database;
   CREATE TABLE users (
       id INT AUTO_INCREMENT PRIMARY KEY,
       name VARCHAR(50),
       email VARCHAR(100)
   );
   INSERT INTO users (name, email) VALUES ('John Doe', 'john.doe@example.com');
   SELECT * FROM users;
   

   1. Using Workbench:
   • Connect to your MySQL Server.
   • Create a new schema (database) through the GUI.
   • Use the built-in query editor to run SQL commands.

You might be wondering why we’re focusing on SQLite as an experience-gaining component and not something more robust like Oracle or Microsoft SQL Server. This is because SQLite is simple to set up and use.

In contrast to other DBMSs that require a server, SQLite stores data directly on your hard disk as a single file. This makes it easy to manage and access data without complicated server configurations. It’s also perfect for real-world small SQL projects, such as mobile apps or simple desktop applications.

Here is how to get started with SQLite:

Step 1: Install SQLite (if not already installed)

• For Windows: Download SQLite from the official website.
• For macOS: SQLite is usually pre-installed. You can check by typing sqlite3 in the terminal.
• For Linux: Install SQLite using the package manager (e.g., sudo apt-get install sqlite3 on Ubuntu).

Step 2: Open the SQLite Command Line Tool

• Windows/macOS/Linux: Open the command line (or terminal) and type. This command will open the SQLite shell and create a new database file named my_database.db. If the file already exists, it will open the existing database.

  sqlite3 my_database.db
  

  Step 3: Create Tables in the Database

  Once you’re inside the SQLite shell, you can create tables to store your data. For example, to create a table for storing users:

  CREATE TABLE users (
  id INTEGER PRIMARY KEY,
  name TEXT NOT NULL,
  email TEXT NOT NULL UNIQUE
  );
  

Step 4: Insert Data

You can insert data into your table using the INSERT INTO command:

INSERT INTO users (name, email) VALUES ('John Doe', 'john.doe@example.com');

Step 5: Verify the Data

To check if your data was inserted correctly, run:

SELECT * FROM users;

Step 6: Exit the SQLite Shell

Once you’re done, type .exit to exit the SQLite shell and return to your command line.

You can now use your my_database.db file as your SQLite database for any project.

Practicing SQL queries that are commonly asked in interviews is a great way to prepare for real-world applications. Many beginner-level data analyst or developer interviews include SQL questions to assess your ability to retrieve, manipulate, and analyze data. Here is how to approach the Interview Query SQL Questions:

Understand the Core Concepts

As mentioned, focus on mastering core SQL concepts like SELECT, WHERE, JOIN, GROUP BY, ORDER BY, and aggregation functions (COUNT, SUM, AVG, etc.). These are frequently utilized in real-world projects and tested in interviews across various industries.

Work with Joins

Most SQL projects involve writing queries that require you to join multiple tables. Get comfortable with SQL problems regarding different types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN) and understand when to use each type based on the given dataset.

Master Subqueries

Subqueries are often used to filter or aggregate data before it’s processed by the main query. Practice writing subqueries in WHERE or FROM clauses, and learn how to optimize them for better performance.

Practice Real-World Scenarios

In addition to technical SQL skills, practice with real-world datasets to analyze them and produce meaningful insights. For example, you might start with writing queries that generate sales reports, calculate averages, or identify trends over time. Practicing such questions will prepare you for real-world applications.

Optimize Queries

Performance optimization is also an important part of SQL experience. Companies in 2024 want candidates who can not only write correct SQL but also ensure that queries are efficient and can handle large datasets. Practice techniques like indexing, minimizing subquery usage, and using EXPLAIN to analyze query performance.

Whenever you’re fixated on doing a real-world SQL project to gain experience and build your portfolio, it’s essential to understand your motivations and end goals. This helps drive the direction and effectiveness of your project.

For example, Luna Doan, in her Medium article, offered us a sneak peek at how she uses SQL at work. In the article, she describes how she used SQL to solve a business problem involving customer service during outages. The product team was focused on minimizing disruption, and Luna used SQL to analyze data from the data warehouse to uncover insights that helped streamline services.

Understanding the “why” behind a project, as Luna did, helps keep the focus on solving tangible, real-world problems.

Understanding how to structure and organize data for reporting purposes is a critical skill in SQL. A report schema typically includes tables designed to store data in a way that facilitates easy querying and reporting. Here’s how to approach it:

• Design the Database: Plan out your database schema, including tables, relationships (foreign keys), and primary keys.
• Normalization: Ensure that your schema is normalized to avoid data redundancy, which is crucial when designing for reporting.
• Reporting Queries: Write complex queries to generate reports based on aggregated data, applying GROUP BY, HAVING, and JOINs for multi-table reports.

Building a report schema will help you get comfortable with designing databases for analytical purposes. You may also refer to these interview questions to get an overall idea about how project schemas work:

1. Let’s say you’re setting up the analytics tracking for a web app. How would you create a schema to represent client click data on the web?
2. Let’s say you’re setting up the analytics tracking for a web app. How would you create a schema to represent client click data on the web?
3. Let’s say you work at a company developing a new ride-sharing app. How would you design a database that could record rides between riders and drivers? What would the schema of the tables look like, and how would they join together?

While hands-on practice is essential, getting certified can also boost your credibility and help validate your skills. Certifications offer structured learning paths and are often recognized by employers. Some popular SQL certifications include:

• Microsoft Certified: SQL Server Database Fundamentals
• Oracle Database SQL Certified Associate
• Google Data Analytics Certificate (includes SQL)

These certifications not only test your knowledge of SQL but also motivate you to study more deeply and consistently.

Networking with other SQL professionals can accelerate your learning. Join online forums, groups, or local meetups (such as Stack Overflow, Interview Query, SQLServerCentral, or r/learnSQL on Reddit). Seeking mentorship from experienced data analysts, developers, or database administrators can help you solve problems faster and learn best practices.

By discussing challenges, sharing projects, and asking for advice, you’ll gain insights that may not be easily found through solo practice.

Gaining SQL experience as a beginner involves learning the basics, practicing on demo databases, working on real projects, and preparing for interviews. By applying SQL skills to solve real-world problems, seeking mentorship, and building a portfolio, you can improve your proficiency. Continuous practice, networking, and certification will enhance your ability to handle complex data challenges and succeed in the field. All the best!