In its role as a query-based language used to communicate with and manipulate data held in a database, SQL relies on other languages to process that data. In doing so, it gives rise to the question, how has SQL influenced other languages?
SQL has influenced other languages in querying data in a relational database, functioning as a declarative programming language. Procedural programming language functionality is obtained through extensions and with SQL integration with other languages and sublanguages.
This article will look at some of the languages and sublanguages influenced by SQL since it was first developed in 1974. It will also examine how SQL integrates with Tableau and Python.
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SQL’s Influence as a Domain-Specific Language
To appreciate how SQL has influenced database management systems, you must first distinguish it from general-purpose computer languages. SQL is a domain-specific language. Unlike a general-purpose language, SQL focuses on one domain—communicating with and manipulating relational databases.
In this sense, SQL functions in a similar capacity to an application programming interface (API). SQL helps to define the interaction between different intermediaries in a complete relational database management system. While this role would usually imply extreme standardization, the reality is that different SQL implementations are rarely compatible across vendors.
Despite this, due to its efficiency in accessing multiple records with a single command and extracting records and parsing file names from their full file paths, SQL has become the de facto language for interacting with data in a relational database management system.
This entrenched popularity and mass diffusion across database management professionals have resulted in SQL’s influence on other languages.
Role of Standardization in SQL’s Influence
SQL was adopted as a standard by the American National Standards Institute (ANSI) and the International Organization for Standardization (ISO) in 1986 and 1987, respectively.
These standards only require SQL database programs to support the following main commands to comply:
In terms of actions to be performed, this standardization only requires SQL implementations to execute queries, create tables, insert and delete records, create new databases, retrieve data, set permissions, and produce stored procedures.
The fact that SQL standards are broad as opposed to specific has resulted in most SQL implementations to be very similar. Simultaneously, however, these same standards have also allowed for incompatibilities to exist across different vendors, implementations, and SQL dialects.
Nevertheless, Standard SQL’s influence on each variant and SQL dialect in existence is evident in the commands and syntax found in the most commonly used SQL RDBMS programs. The command structure and specific syntax may differ just enough to make true compatibility an issue; however, they are similar enough to appreciate Standard SQL’s influence on each of them. These include:
- Oracle Database
- MS Access
- MS SQL Server
Other Languages Influenced by SQL
The influence of SQL can also be seen in programs that are meant to deal with unstructured data as opposed to relational databases.
Apache Hive, for example, relies on a language called HiveQL. This language is heavily influenced by SQL in its command syntax.
Another example is SparkSQL. This language is used in the Spark Project from Apache. SparkSQL can be used to query data in a Spark Project database using SQL queries. These queries include data stored in non-relational databases such as Apache HBase or the Hadoop Distributed File System (HDFS).
SQL-Mapreduce, the framework used for complex in-database analytics, is also influenced by SQL. You can appreciate the extent of this influence in the way that SQL-Mapreduce functions can be invoked using standard SQL.
SQL Complimenting Other Languages
You can appreciate the influence of SQL not only in its direct form, as listed above, but also in how it complements other languages.
By complementing other languages, SQL allows database management systems to offer many analytical and visualization options. It also provides for a smoother process when conducting a more in-depth analysis in more extensive databases.
Two clear examples of SQL complimenting other languages come in the way it is used in conjunction with Python and Tableau.
Is SQL Necessary for Python?
Python is an interpretable general-purpose programming language. As such, you can use it to create code for a wide variety of projects of all sizes. These include database and data analytics projects, among others.
Specific to databases and analytics, Python is commonly paired with SQLite, a relational database management system. SQL is then used for structuring and interacting with the database.
Therefore, while SQL is not necessary for the multitude of projects that can be created with Python, there is synergetic cooperation between them when it comes to database management.
Is SQL Necessary for Tableau?
Tableau encompasses a collection of software offered by the Salesforce subsidiary of the same name.
This software focuses on applications for the interactive visualization of data. These applications need to query data from spreadsheets, other flat files, and relational databases.
Using SQL as the liaison between the relational databases where the data is stored and Tableau, you can join data from multiple tables to improve the depth of the analytical data that is being presented. You can also use custom SQL queries for advanced filtering of data or to handle different data sources on the same query.
That being said, SQL is not required to use Tableau for the creation of data visualizations. It is possible to pull data in Tableau from databases using the data connection screen on Tableau’s user interface. While not as robust as using your custom SQL queries, the interface does make it possible to use Tableau without SQL.
If the data sources for the visualizations consist of flat files instead of databases, SQL becomes overkill when working with Tableau. The local interface is more than sufficient for that magnitude of a task.
However, whether you use SQL to pull data into Tableau or rely on its interface, you are experiencing either the direct or indirect influence SQL has had on the software.
The commands used in SQL are divided into six categories. These categories are also known as sublanguages. Since technically they are simply segments of SQL as a whole, they are not stand-alone languages.
These sublanguages are:
- Data Definition Language (DDL): These include the commands that create and define tables and databases.
- Data Manipulation Language (DML): Commands for updating, modifying, or deleting data are part of DML.
- Data Retrieval Language/Data Query Language (DRL/DQL): As the name implies, querying data commands are part of this sublanguage.
- Transaction Query Language (TCL): This covers commands for committing and restoring data.
- Data Control Language (DCL): Commands for who can access the data belong here.
- Session Control Language (SCL): Commands for controlling a specific user’s session’s properties are covered in this category.
You should not see the addition, removal, or expansion of commands within each sublanguage as the result of external influence from SQL. After all, these sublanguages are part of SQL. Changes to SQL sublanguages are organic to SQL itself.
When SQL sublanguages can be associated with “influenced change,” is when changes in the sublanguages related to one SQL implementation resonate and bring about similar changes in the sublanguages of other SQL implementations.
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Since SQL was first developed in 1974—and growing when it became a standard under both ANSI and ISO protocols in the mid-1980s—it has influenced other languages and applications related to relational database management systems.
The influence of SQL is evident in similarities in commands and syntax. It can also be seen when incorporated into a relational database management system as a critical component for using advanced queries when working with data. Even when SQL serves as an optional interface for data queries, such as Tableau, it still influences the workflow associated with pulling and organizing data.
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