SQL, or Structured Query Language, can sound intimidating at first. It’s a language primarily used to define, manipulate, and query data held in relational databases — the kind of databases where data is highly organized and structured to fit in well-defined rows and columns.
Note: You can learn more about relational databases, including their history, key concepts, and common uses, by following the Understanding Relational Databases tutorial.
SQL’s popularity has grown since its inception in the 1970s, and the areas where SQL is used have vastly expanded. Now, SQL is a mature and prevalent way of querying and manipulating data in most industries and with the most popular tools. You have a high chance of encountering SQL in your work, whether you are a database administrator, database architect, software engineer, data analyst, or none of the above! Even if you don’t use SQL directly, you can still benefit from understanding its concepts.
This article shows why it’s worth learning SQL and explains where and how you can apply that knowledge.
If you come across any relational database, such as MySQL, PostgreSQL, Microsoft SQL, Oracle SQL, or many others, you will inevitably stumble upon SQL statements. The language is widely used to define, manipulate, and query data in this type of database. It’s the primary way of interacting with the database engine.
Although many database systems provide easy-to-use GUI tools to help work with structure and data, they don’t make SQL obsolete. While the simplest of tasks can be quickly achieved without SQL, more complex data manipulations or querying will inevitably require using SQL to construct queries.
With SQL, you can query the data that you need and do so efficiently, playing into the strengths of the database engine.
By understanding SQL, you’ll be able to quickly get up to speed with virtually any relational database system used today. Your skillset will not be limited to any particular software or employer.
Because of its popularity and use across widely adopted relational database systems, SQL has found its way into other database systems and data analysis tools.
SQL and SQL derivatives are supported across many data storage systems, data analytics engines, business intelligence tools, and data mining tools, including many non-relational databases, analytical (OLAP) databases, and big data solutions.
Whatever software you might encounter for analyzing large datasets, there is a good chance you can use your SQL knowledge to work with data across those tools. You will be able to use a similar approach with different databases, making the work more accessible and more universal across different data sources. As a result, SQL knowledge is a primary and essential tool that is frequently used by data analysts and data scientists.
SQL is even in the top ten most popular programming languages in the TIOBE Programming Community index, an indicator of the popularity of programming languages.
Discussing data can often be challenging, since people may understand everyday language slightly differently. This ambiguity can lead to misunderstandings and errors in communication. Knowing SQL basics, such as understanding how the data is structured and how you could query it yourself, you can be more precise when communicating with your colleagues and team members.
Even if you are not going to write SQL statements yourself, you can use your experience to convey your requirements and expectations precisely. You will also be able to pinpoint issues with data that you receive and provide easy-to-understand and actionable feedback to the data analysts who helped prepare the data for you.
You can think of SQL as a common language universally understood by the people working with data. Even if you do not use SQL directly, referring to concepts from SQL will get your communication on track.
When tasked with designing any database, it’s important to consider what kind of data will be stored in the database and how it will be accessed or manipulated in the future. While it is certainly possible to design databases based exclusively on a good grasp of database design theory, moving from pen and paper to the actual database can be difficult and can often hold surprises.
Whenever any data is retrieved from a database, some kind of SQL query will be used, either written by the data analyst or generated through software. By understanding the desired usage patterns and knowing how to translate them into possible SQL queries, you will grasp how SQL will access the underlying database to retrieve the data, and what the database engine will have to do to respond to such a query.
In turn, you can use that knowledge to design databases fit for the purpose. By taking into account the use cases the database should support, you can choose the database structure that lends itself to simpler and more efficient queries for common scenarios.
You can structure tables thoughtfully as well as use data types, foreign key relationships, and indexes to facilitate data access. In effect, you’ll learn how to design databases better suited for your purpose.
Modern software development frameworks and popular web frameworks, such as Laravel, Symfony, Django, or Ruby on Rails, often employ data abstraction layers like object-relational mappers to hide the complexity of data access from the developer. In general, you will not use SQL directly to access or manipulate data when working with such frameworks. The easy-to-follow syntax typical to the framework will make things “just work” for you, and the requested data will become seamlessly available.
However, no matter how many shortcuts and simplifications frameworks provide, the underlying database engine will be queried using an SQL statement constructed under the hood from your input.
Understanding how SQL works can help you use the features of your chosen framework to make queries faster and more efficient. You can use your knowledge of how queries are built by the framework to influence the construction and execution of queries that are the best way to access the data.
You can also more easily debug any issues with data querying and manipulation. Database engines often return errors referring to the failing part of the actual SQL query executed, which, while precise, makes it difficult to backtrack the issue to the framework-specific query syntax and where the issue comes from. By understanding database errors, you can precisely pinpoint the issue at hand.
Last but not least, you will also be more away of security dangers that stem from any improper and dangerous use of SQL queries, such as SQL injection attacks, and you will be able to counteract them.
By knowing SQL, you will be fully in control of how you access the data, regardless of whether you use SQL directly or choose to work with software abstractions and ORM tools within software frameworks.
There are many benefits to understanding and applying the Structured Query Language in practice, but the best part is its accessiblility for beginners. It is well-defined, and its syntax primarily uses common English words to name operations, filters, and other modifiers. SQL queries can often be read like English sentences and quickly understood even without prior programming experience.
There are more challenging and complex aspects of the language that can prove tricky and require a good deal of work to comprehend and gain experience with. But the essentials of SQL can be understood and learned at a more basic level. By using it in your daily work, it can be convenient to learn with your actual data needs. You can start with the most basic concepts of SQL and extend your grasp of the language whenever you need to retrieve some data in a way you haven’t done before. Experimenting with SQL is straightforward and non-destructive when querying data, making it safe and reassuring.
By learning SQL, you can gain benefits that greatly exceed the time involved, acquire new methods to retrieve and analyze data from multiple sources, become self-sufficient in data analysis, and open new career paths in multiple fields.
Due to its flexibility, ease of use, and applicability within different data-related areas, SQL is a ubiquitous data querying and manipulation language. Learning it can have many benefits, even if your primary job is not directly related to databases or creating software.
To get started using SQL, check out the How To Use SQL series, which covers a range of topics from introductory articles on various SQL concepts and practices to advanced techniques and features of the language. We encourage you to follow this tutorial series to get acquainted with SQL. You can also use the entries in this series for reference while you continue to hone your skills with SQL.
To practice and experiment with SQL without installing and configuring the database server yourself, you can use one of the DigitalOcean Managed Databases (either Managed MySQL or Managed PostgreSQL), which provide a quick path to a fully working database environment.
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Structured Query Language — commonly known as SQL — is a language used to define, control, manipulate, and query data held in a relational database. SQL has been widely adopted since it was first developed in the 1970s, and today it’s the predominant language used to manage relational database management systems.
Ideal for managing structured data (data that can fit neatly into an existing data model), SQL is an essential tool for developers and system administrators in a wide variety of contexts. Also, because of its maturity and prevalence, candidates with SQL experience are highly sought after for jobs across a number of industries.
This series is intended to help you get started with using SQL. It includes a mix of conceptual articles and tutorials which provide introductions to various SQL concepts and practices. You can also use the entries in this series for reference while you continue to hone your skills with SQL.
Note: Please be aware that the tutorials in this series use MySQL in examples, but many RDBMSs use their own unique implementations of SQL. Although the commands outlined in this tutorial will work on most RDBMSs, the exact syntax or output may differ if you test them on a system other than MySQL.
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