What is SQL?

Definition

SQL, or Structured Query Language, is a standardized programming language specifically designed for “querying” or managing relational databases. In simple terms, it lets users ask questions of databases as well as update records, insert new data, and delete existing data. SQL also supports advanced functions like joining tables, aggregating data, and creating or modifying database schemas. Thanks to its user-first architecture, users are able to specify what data they need without detailing how to retrieve it, making it both powerful and user-friendly. Today, SQL is used globally across industries for data analysis and database management, making it a foundational tool for data-driven decision-making.

Who Created SQL?

SQL was developed in the early 1970s by IBM researchers Donald D. Chamberlin and Raymond F. Boyce. Known originally as SEQUEL (Structured English Query Language), it was designed to manage one of the first-ever relational databases at IBM. Thanks to its simplicity and effectiveness in handling structured data, the programming language quickly gained popularity and was standardized in 1986 by the American National Standards Institute (ANSI) and in 1987 by the International Organization for Standardization (ISO). These standards ensured consistency across different database systems, fostering widespread adoption. 

Over the years, SQL has evolved to include new features and capabilities, such as support for XML, JSON, and even advanced analytics. In fact, the language has proved so popular that different dialects have emerged in support of newly developed relational models. Today, SQL remains a cornerstone of database management, powering systems like MySQL, Oracle, and Microsoft SQL Server, and is integral to modern data-driven applications.

How does SQL work?

For anyone working in data analysis, SQL is an essential tool for extracting meaningful insights from raw data. Although it has gained increased functionality since its inception, SQL’s four core functions are querying, updating, inserting, and deleting data. One of SQL’s most attractive features is its user-friendly architecture, which allows users to request information without explaining how to retrieve it. Better yet, thanks to its standardization and widespread adoption, it integrates seamlessly with other programming languages like Python and R.

Completing a SQL query involves several key components. First, the query must be written and run (or parsed) through a query optimizer. Once the query runs and reaches the SQL server, it undergoes three main phases: parsing, binding, and optimization. During parsing, the system checks the query’s syntax to ensure it is correctly formatted. In the binding phase, the system verifies the query’s data specifications, ensuring that the referenced tables and columns exist. Finally, in the optimization phase, the system evaluates all possible ways to execute the query and provides the most efficient execution plan. With every query, the goal is to minimize execution time and resource usage, ensuring optimal performance.

For example, consider a database of customers for an e-commerce store. To retrieve all customers from the U.S., you could use the following SQL query:

SELECT * FROM Customers WHERE Country=’USA’;  

This query instructs the database to select all rows (*) from the Customers table where the Country column matches ”USA.” The result is a filtered dataset that can be used for further analysis. For example, a user could then choose to segregate the data further by city or even zip code. 

Why is SQL Important?

SQL plays a critical role in organizing, accessing, and interpreting data. Its importance stems from several key factors:

1. Data Management: SQL is the standard language for managing relational databases, which are the backbone of most data storage systems. Almost every business managing customer databases uses SQL in some way.
2. Standardization and Interoperability: Thanks to its standardization by ANSI and ISO, SQL is consistent across countries and industries, meaning it can be used seamlessly across platforms. Its universal syntax also ensures that data professionals can work with diverse databases without needing to learn new languages.
3. Foundation for BI: SQL is essential for extracting and analyzing data, which allows for better insights and data-driven decision-making in any organization.
4. Community Support and Accessibility: SQL has a vast and active community of users, developers, and educators, making it easy to find resources, tutorials, and troubleshooting support. Its simplicity and readability also make it accessible to beginners, while its depth and versatility cater to advanced users. 

Combined, these factors make SQL indispensable for data management, enabling standardization, interoperability, and BI. Its widespread adoption, robust community support, and accessibility make it a vital skill for data professionals and a key enabler of data-driven innovation.

SQL Basics

SQL is built around several components, each of which ensures efficient data management and easy data handling. All SQL commands are used with databases, which form the basis of any dataset. These databases store data in a structured way that can be accessed through proper SQL commands. Each database is then sorted into tables, which organize data by row and column. Queries are the commands used to retrieve or manipulate any of this data, while statements are the actual instructions written in SQL to perform those actions. 

A typical SQL command follows a specified format. For example:

SELECT column_name FROM table_name WHERE condition;  

This command retrieves specific data from a table based on a chosen condition. The data could include customers from a particular region or even those who bought a specific product.

Types of SQL Commands

There are four main types of SQL commands, which together enable users to create, manage, and interact with databases.

1. DDL (Data Definition Language): These types of commands are used to define and modify databases.

Example: 

CREATE TABLE Employees (ID int, Name varchar(100));  

ALTER TABLE Employees ADD Age int;  

DROP TABLE Employees;  

In this example, a user creates a table of employees by ID number and name, specifying the ID as a whole number and a name of up to 100 characters. In the following line, the user changes the table by adding employees’ ages as whole numbers. The last line deletes the entire table from the database, rendering it inaccessible.

2. DML (Data Manipulation Language): These commands are used to manipulate existing tables. 

Example:

INSERT INTO Employees (ID, Name) VALUES (1, ‘John’);  

UPDATE Employees SET Name = ‘Jane’ WHERE ID = 1;  

DELETE FROM Employees WHERE ID = 1;  

In this example, the user inserts a new employee with ID “1” and the name  ”John.” In the second line, they update the employee’s name from “John” to “Jane,” and in the third line, they delete the employee with ID = 1 from the table.

3. DCL (Data Control Language): These commands allow users to manage access and permissions to the data.

Example:

GRANT SELECT ON Employees TO User1;  

REVOKE SELECT ON Employees FROM User1;  

In the above example, the first line allows User1 to read data from the employee table while the second line revokes that access.

4. TCL (Transaction Control Language): This command allows users to manage transactions and ensure data integrity.

Example:

BEGIN TRANSACTION;  

COMMIT;  

ROLLBACK;  

Each of the commands in the example above governs a transaction. BEGIN TRANSACTION starts a transaction, COMMIT saves all changes during the transaction to the database, and ROLLBACK undoes changes during the transaction.

Using SQL for Data Handling

SQL serves as the backbone of database management, allowing users to manipulate and analyze data as needed. It allows for the creation of databases and tables using DDL commands like CREATE DATABASE and CREATE TABLE. Using DML commands like SELECT, INSERT, UPDATE, and DELETE, users can retrieve specific data, add new data, modify existing data, or remove unnecessary data. Users can then use SQL’s powerful querying capabilities to filter, sort, and aggregate data, promoting easy access to data insights. SQL also enables advanced operations like joining tables, grouping data, and performing calculations, which are critical for BI. 

Administrators can use SQL to manage user permissions, both granting and revoking access to certain data sets as needed. TCL (Transaction Control Language) commands like COMMIT and ROLLBACK ensure transactional consistency and a record of change.

SQL vs. NoSQL

Aspect	SQL Databases	NoSQL Databases
Data Structure	Structured and table-based	Flexible with no schema supporting documents, graphs, etc.
Data Integrity	High	Lower
Scalability	Vertical scaling (adding more power to a single server)	Horizontal scaling (adding more servers)
Performance	Optimized for complex queries	Optimized for high-speed operations
Use Cases	Financial systems and inventory management	Social media, real-time analytics, big data
Query Language	SQL	Varies by database

Choosing the Right Database

Choosing between SQL and NoSQL depends on an organization’s data structure, scalability needs, and use cases. SQL databases are ideal for structured data and ERPs. They excel in data integrity and support vertical scaling. NoSQL databases, like MongoDB or Cassandra, are better for unstructured or semi-structured data, offering flexibility, horizontal scalability, and high-speed performance. They suit dynamic data models, real-time analytics, and distributed systems like social media or the IoT.

Consider SQL if you need strict data consistency, complex transactions, or a mature ecosystem. Opt for NoSQL if you prioritize scalability, flexibility, and handling high-velocity data. Either way, using SQL for database management will allow for better and more efficient data insights.

Taking SQL Further: Features and Extensions

One advance to SQL has been the use of stored procedures and automation. Stored procedures are precompiled sets of SQL commands stored in the database, which can automate tasks. These stored commands enable developers to store complex logic sequences, such as data validation, calculations, or multi-step transactions, into a single callable routine. This reduces code redundancy, improves performance, and ensures consistency across applications. For example, a stored procedure can automate monthly sales reports by aggregating data and sending out reports. By centralizing logic within the database, stored procedures streamline operations, enhance security (via access control), and simplify maintenance.

SQL and Programming Languages

SQL is highly compatible with various programming languages, enabling seamless integration into a variety of platforms. Languages like Python, Java, C#, and R even provide libraries (e.g., pyodbc, JDBC, Entity Framework) to connect to SQL databases, execute queries, and manipulate data. Python, for example, can use SQL to fetch data for ML models, while Java applications can leverage SQL for transactional operations. SQL’s ability to interact with APIs, web frameworks, and data analysis tools makes it a universal language for data-driven applications. 

How SQL Enhances Data Accessibility

Integration with Semantic Layers

One advantage of SQL is its accessibility, much of which is accomplished through its integration with semantic layers. A semantic layer acts as a buffer that translates complex database structures into business-friendly terms, enabling users to query data without technical expertise. SQL serves as the bridge between the semantic layer and the underlying database, allowing users to write queries using familiar terminology, like “sales revenue” instead of an exact table name. Tools like Looker and Tableau use SQL to connect to semantic layers, enabling seamless reporting and advanced analytics.

Using semantic layers with SQL offers several advantages:

1. Improved Data Discovery: Users can easily explore and analyze data using familiar terms, reducing reliance on data analytics experts.
2. Enhanced Data Governance: Semantic layers enforce consistent definitions and rules, ensuring data governance, accuracy, and compliance.
3. Faster Decision-Making: Simplified access to data enables quicker insights and actionable BI.
4. Collaboration: Teams across departments can access and analyze data uniformly without fears of versioning or non-compliance, fostering better collaboration and alignment.

By combining SQL with semantic layers, businesses can unlock the full potential of their data, driving efficiency and innovation.

Security and Compliance in SQL

SQL databases offer robust security features to safeguard data. First and foremost are the access controls, which ensure that only authorized users can access or modify data. SQL also supports rigorous authentication policies that include multi-factor and external authentication. Beyond personnel security, SQL also allows for data at rest and in transit to be encrypted to prevent unauthorized access. SQL injection attacks are mitigated through prepared statements and parameterized queries, which separate code from data. Auditing also plays a key part in SQL’s parameters, allowing users to track activities and changes across all data. 

Compliance with standards like GDPR, HIPAA, and PCI-DSS is also critical in SQL operations. These standards mandate strict data handling practices, such as encryption, access controls, and audit trails, to protect sensitive information. SQL databases support compliance by offering features like role-based access, data masking, and logging. Adhering to standards ensures data integrity, builds trust with stakeholders, and avoids legal penalties. Together, these features ensure data confidentiality and compliance, making SQL a secure choice for sensitive applications.

The Future of SQL: Trends and Evolution

Much like the rest of the tech industry, SQL is evolving quickly thanks to the integration of AI and ML, which has enabled advanced data analysis directly within databases. Modern SQL systems, like Google BigQuery ML, allow users to build and deploy ML models using SQL queries, facilitating predictive analytics and real-time insights without having to export the data. Additionally, cloud-native SQL databases and real-time processing capabilities are enhancing scalability and performance, making SQL a key player in handling large-scale, dynamic datasets. Thanks to innovations like these, SQL continues to build on its existing functionalities to provide better data insights. 

SQL’s standardized syntax, robust transactional support, and integrations with modern technologies like AI ensure its continued relevance despite the rise of NoSQL and other technologies. Its ability to handle complex queries, ensure data integrity, and integrate seamlessly with programming languages and BI tools makes it indispensable for structured data analysis. Further, SQL’s adaptability to emerging trends like AI and cloud computing demonstrates its versatility. As long as data remains a key component of BI and innovation, SQL will remain a foundational technology in the tech industry.

Bottomline: SQL Makes Modern Database Management Possible

SQL plays a critical role in modern data management and business operations by providing a standardized and easy-to-use language for managing structured data. Its ability to handle complex transactions, ensure data integrity, and integrate seamlessly with various tools and platforms makes it indispensable for any business. Further, SQL’s compatibility with emerging technologies like AI, ML, and cloud computing enhances its position as a foundational tool for driving data-driven decision-making, operational efficiency, and innovation.

How AtScale Can Help

Integrating SQL with semantic layers can revolutionize your data strategy, and AtScale leads the way with its semantic layer platform. By simplifying enterprise data management, AtScale delivers consistent, secure, and real-time insights across BI and AI tools without the need to move data. This approach bridges complex data infrastructures with end-user applications, empowering teams with streamlined governance, accelerated analytics, and cost-effective scalability. Transform your data strategy today by exploring how AtScale can unlock the full potential of your data. Contact us today to schedule a free live demo.