Python and SQL are the two most common programming languages crucial in the day-to-day work of data engineers and scientists. So for anyone looking to delve into data, choosing one of these languages to learn and master is typical.

Understanding the nature of both languages, what they offer, and their advantages can help budding data professionals decide which language to learn. So let’s dive into the world of Python and SQL.

You can’t speak about data exploration and manipulation without Python. Its versatility, ease of use and integration, and flexibility make it a ubiquitous programming language for software programmers and data professionals. Guido Van Rossum developed Python in the late 1980s under the OSI-approved open-source model. Its readability and easy-to-follow syntax make Python a common language recommended for beginners to build web applications.

Python is used today to build backend applications, Application Programming Interfaces (APIs), system scripts, and data applications. 

Python is a favorite for working with data because its easy integration with multiple libraries and flexibility make it easy to adapt to various formats (text, video, audio, Comma Separated Values (CSV), and web) involved with working with data.

Today, successful companies like Netflix, Uber, Pinterest, Instagram, Spotify, and numerous others leverage the power of Python for their services. In addition, Python also helps build and power sophisticated applications like machine learning, augmented reality, autonomous vehicles, Android mobile apps, and iOS mobile apps.

Python at a glance

Here are some of the well-known features of Python;

• Free and Open-source: The Python source code is available to the public for downloads and installs at no cost. Additionally, because Python is open-source, it contains a large community of developers, promoting network and community building, which helps provide extensive support for beginners and bug fixes.
• Dynamically-typed: Languages like Python and Javascript are dynamically-typed, meaning that the interpreter assigns variables its type at runtime versus compile time. This feature introduces flexibility to the Python language.
• Easy to read and understand: Python syntax is simple and easily readable. Instead of using curly braces like other languages, it uses indentation for code blocks.
• Object-oriented Programming language: Python utilizes object-oriented programming that revolves around solving and implementing solutions with objects. OOP improves the reusability of code.

Structured Query language (SQL) resulted from an IBM research proposal titled “A Relational Model of Data for Large Shared Data Banks” in 1970. Dr E. F. Codd, a research team member, proposed that data representation should occur using tables. IBM later formed a team that created the first version, SQUARE, improved the language, and changed the name to SEQUEL, later shortened to SQL.

Since a database is a collection of multiple tables, a table provides the framework for SQL implementation. SQL helps communicate with databases.

SQL is a programming language used to build, store, and retrieve data from data management systems. SQL allows data professionals to retrieve records from databases and generate powerful insights crucial for business decision-making.

SQL at a glance

• Enables Database Communication: SQL is the programming language that helps design, assess, maintain, protect, and maintain SQL databases.
• Declarative Language: SQL utilizes a declarative programming approach by describing what a program does without controlling its workflow.
• Wide range of usage: SQL is a popular programming language and adapted by almost all Relational Database Management Systems(RDMS) like MySQL, MS Access, Oracle, Postgres, and SQL Server.
• Easy syntax: SQL has a straightforward syntax and can be easy to learn and understand, even without any prior programming knowledge.
• Wide range of commands: SQL supports DQL (Data Query Language) commands like SELECT; DDL (Data Definition Language) commands like CREATE, DROP; DCL (Data Control Language) commands like GRANT, REVOKE; DML (Data Manipulation Language) commands like INSERT, UPDATE, DELETE; and TCL (Transaction Control Language) commands like COMMIT, ROLLBACK.
• Scalability and flexibility: As stated earlier, SQL can help add new tables, edit new tables, and delete old tables that are no longer in use. Hence, it can scale up/down to accommodate datasets according to business needs.
• Integrations with other non-SQL databases: SQL uses a third piece of middleware called an ODBC driver to connect to non-SQL databases like Oracle and Salesforce.

Performance

For simple queries and aggregations, SQL performs faster than Python because the data in the database already has a defined schema, and the computation process occurs close to the data. For Python, data extraction and loading must occur before data exploration, which may introduce latency. Additionally, Python is a scripting language that needs to execute multiple programming instructions at runtime.

Functionality

Python offers a broader range of functionality than SQL with its ecosystem of third-party libraries, making it applicable to many applications like Machine Learning, exploratory data analysis, and API development. For SQL, there are limited packages to help improve functionality.

Scalability

SQL can scale up according to increasing datasets by adding more tables or deleting old tables in a database, thereby improving scalability. Multithreading is a technique that helps divide a process into multiple threads and enhances applications’ ability to handle increasing requests. Unfortunately, unlike languages like Java, which uses multithreading, Python uses GIL (Global Interpreter Lock), which prevents the Python interpreter from performing multiple tasks concurrently, impacting the speed and scalability of applications.

Testing

Running tests are crucial to data pipelines and help ensure code works as intended, without bugs, and is maintainable. Like most programming languages, Python offers extensive unit and integration tests for parts of the data processing pipeline, from data queries to machine learning models and complex mathematical functions. On the other hand, SQL offers no extensive unit testing.

Ease of use

Although Python has a straightforward syntax, SQL is more beginner-friendly, has fewer concepts, and is easier to learn.

Roles

For data scientists who perform a wide range of tasks like cleaning, manipulation and exploration, possessing Python programming skills will help them perform daily tasks. On the other hand, data engineers and analysts require extensive SQL skills to help manage and monitor ETL tasks in databases and data modeling. There is, however, certainly an overlap in skill sets for all of these roles.

The StreamSets platform helps organizations work with both Python and SQL-driven processes. For example, with Jython processors, data engineers, data scientists, and subject matter experts can engineer data processing pipelines before conducting analysis. In addition, StreamSets provides its own Python Software Development Kit (SDK), with multiple built-in functionalities to enable developers and engineers to work with their data to produce insights promptly. Using the Python SDK, organizations can automate repeatable processes within their pipelines with a keystroke. 

StreamSets also supports SQL semantics in processors which allow users to write SQL queries to extract, transform and load data. StreamSets also supports advanced database operations like Change Data Capture (CDC) for further continuity for SQL experts.

Building on the power and capabilities of Python and SQL, StreamSeta enables organizations to build robust, intelligent data pipelines that help power both simple and complex applications.

Do data engineers use Python or SQL?

Generally, data engineers use both SQL and Python. They use SQL for querying relational databases and Python to process and analyze data and create scripts.

Are Python and SQL Enough for data science?

Python and SQL can, but don’t always, provide sufficient capability for the tasks of a data scientist. When the project calls for it, data scientists may enlist other languages and tools such as R, Scala, Java, and others.

Can Python replace SQL?