SQL vs. Python: Everything You Need to Know

With rapid advancements in data science and an exponential rise in the volume of data to analyze, data storage and retrieval facilities have become more accessible over the last few years. Today, when data comes in various formats and sizes – CSV files, software languages, text files, JSON, Protocol Buffers, Parquet, etc. – not essentially analogous to relational databases, data scientists no longer have to depend on a few programming languages for prompt data insights, record consolidation, and drawing conclusions. They have a whole host of coding languages to pick from.

And two of the most used programming languages are SQL and Python, each with specific use cases in data manipulation.

Let’s dive deeper into SQL Vs. Python and how they can benefit you.

What is SQL?

Developed by IBM researchers Donald D. Chamberlin and Raymond F. Boyce and released in 1979, SQL/Structured Query Language is a domain-specific coding language best for stream processing and operating and manipulating data kept in a relational DBMS – database design, query, deletion, manipulation, row modification and fetching, data accessing and retrieval, etc.

It is also great for generating real-time data insights, executing data analytics, and recovering records from tables within an expansive ecosystem of relational databases. SQL acts as the fundamental database language for almost all RDBMS (Relational Database Management System), for instance, Microsoft Access, MySQL, IBM DB2, Informix, SQL Server, Sybase, Oracle, etc. 

Features of SQL Query Language

• Includes command to define and describe data – Data Definition Language (DDL)
• It supports commands for data manipulation – Data Manipulation Language (DML)
• High-performance coding capacity for heavy workloads, highly transactional, and high usage databases
• Open-source language 
• Portable across different computer systems
• It features vertical scalability – it lets you expand SSD, CPU, and RAM capacity to add more loads on a single SQL server.
• Highly secured

What is Python?

Designed by Guido van Rossum, Python is a high-level, multi-purpose, and multi-platform programming language with extensive use. There are hardly any tech-based tasks Python cannot excel at – software development, data visualization, data wrangling, website and game development, data analysis, Machine Learning, etc. 

The dynamic semantics coupled with unit testing frameworks, dynamic typing, and dynamic binding make Python suitable for working on RAD models (Rapid Application Development). 

Python’s readable syntax emphasizes natural language and can significantly reduce program maintenance costs. 

Features of Python

• Python is a free and open-source language that enables programmers to tinker with source codes when required. 
• Comes with high-level data structures
• Supports four major programming paradigms – functional, imperative, open-source and 
• Python is an interpreted language, meaning that the Python interpreter converts source code into bytecode instructions (similar to machine code) before the PVM/Python Virtual Machine executes it. 
• It is extensible to other coding languages like C/C++.
• Python is highly portable and can be integrated with other coding languages, for instance, Java, C, and C++.
• Highly secured

SQL vs. Python: The Key Differences

Performance

SQL is a declarative querying language, and Python is a general-purpose language. If you employ SQL for querying, manipulating, and running computations on structured data in a relational database, the processing will be speedier than in Python, provided that it involves no intricate calculation or in-depth analysis. 

Python has to retrieve data from a specific database and feed it into the local memory before operating on it. It, by default, introduces some degree of latency and reduces the processing speed in Python. As SQL acts on your data in place, the latency is less here. 

However, Python’s data analysis performance can get a significant boost when coupled with Pandas – Python’s robust data analysis, manipulation, and structuring tool. 

While dealing with more convoluted data aggregation or computations that require time-series tests, regression testing, statistical analysis, and intricate data science, SQL performance may suffer. 

Functionality

Being a domain-specific language, SQL may perform better than Python while performing simple data querying from relational databases, but it comes with limited functionalities. But if you go with SQL to execute in-depth data analysis on an extensive dataset, Machine Learning, or iterative EDA (exploratory data analysis), the codes will get more verbose, adding significant lagging. It’s because SQL dialects are somehow restricted, and there are tasks SQL cannot handle, or at least get highly intricate and convoluted in SQL. 

SQL queries operate based on task-specific functions. There is no inbuilt package manager in SQL you can call pre-built functions from. It significantly restricts SQL’s functionality and usage. On the flip side, Python downloads with a slew of standard libraries and built-in functions – only plug functions from Python libraries into your code. With more than 137000 existing libraries, Python is now the most burgeoning coding language, having scopes in a spectrum of fields, including data visualization, data science, Machine Learning, data and image manipulation, and the sky’s the limit. 

Unit Testing

Unit testing is inevitable to ensure the source of the application you develop is bug-free and can function accurately. 

As a multi-purpose system, Python serves developers with a whole host of unit testing frameworks like PyUnit, PyTest, etc., to enable them to run unit tests on any portion of their data processing pipeline – from web scraping and data query to more intricate computations. 

For SQL, unit testing is somehow limited – you can conduct dbt tests once the SQL model is materialized, but its functionality is not as superior as Python. 

Debugging

Python again wins the race while considering code debugging. 

In Python, you can set up a conditional breakpoint and single stepping at the source line level to limit the time and effort you need to give to track down the causes your code fails. SQL doesn’t allow you to fix a breakpoint in a code – you have to run an entire statement and find errors. 

Though debugging the query in the Common Table Expression (CTE) or dividing dbt models into several files can make debugging SQL intermediate models a bit effortless, they are not as robust as Python debugging techniques. 

When to Use SQL and Python?

SQL is like a system of generalized lego sets containing an ecosystem of limited and simplified commands. On the contrary, Python acts as specialized lego sets of ever-mushrooming modules for facilitating distinct tasks. 

Let’s review and sum up where SQL and Python suit the most: 

• If you are working with structured data in a relational database that includes simplified analysis, and data re-correction, SQL can be your pick – performing the initial query for data retrieval in SQL is more effortless than in Python. Plus, for merging multiple datasets, SQL is second to none. However, for more in-depth data breakdowns, you can go with SQL for the initial query for tabular data retrieval and choose Python for its top-notch flexibility to run high-level manipulation, experiments, and analytics on the extracted data. 
• Again, if you are up for designing web and mobile applications, back-end software programs, games, and more, Python, the well-scripted coding language, should be your go-to choice. 

Which Language to Learn First?

In data science, Python and SQL go hand in hand. Python is a chart-topping scripting language that, by coupling with SQL, can help data scientists dig deeper into complex datasets and make the most out of them by enabling interactive data visualization and facilitating high-level data manipulations.

However, as a beginner interested in data analytics, you can jumpstart your programming skills with SQL. The pretty straightforward basics and readable, smaller syntax in SQL make it effortless to grasp.

As mastering data retrieval is the initial stage of getting the hang of in-depth data analysis and manipulation, the practitioner-level skill of SQL will set you up for becoming a Python whizz with ease.