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What is SQL vs. Python

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SQL and Python are programming languages that data scientists and data engineers need. If you are interested in the data field, consider mastering one of the languages. It is important to understand what the two languages entail, their advantages, and everything they have to offer data professionals.  


When you think of data manipulation and exploration, you must think of Python. This is a flexible language and relatively easy to use. It is also pretty versatile, making it one of the languages of choice for data professionals and software programmers.

Pythons application creates data applications, APIs or programming interfaces, and back-end applications. It is a good choice for anyone who works with data. It is to integrate. In addition, it has many libraries and is very flexible. This means it can adapt to different formats, including:

  • Web
  • CSV or comma-separated values
  • Audio
  • Video
  • Text

There are many companies today that use the power that Python has to leverage the services offered. The companies include Spotify, Instagram, Pinterest, Uber, and Netflix. Python also makes it easy to power and build some of the most sophisticated applications such as IOS mobile apps, android mobile apps, autonomous vehicles, augmented reality, and machine learning. 

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Python Features

Some of the features that Python has to offer include:

  • Open source and free: the source code for Python is available for installation and download to the public without any cost. This open-source programming language has a vast community of promoting networks, community building, and developers. This helps with bug fixing and is the best support, especially for beginners. 
  • Dynamic typing: JavaScript and Python are languages that are dynamically typed. Variable type can be assigned their compile-time and run-time. This makes Python such a flexible language. 
  • It is an easy language to understand and read: Python has a readable and straightforward syntax. For example, curly braces are not used but instead, indentation for the code blocks. 
  • Python is object-oriented: Python deals with implementing and solving all sorts of solutions using objects. Naturally, therefore, the re-usability code is improved by OPP. 

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SQL means structured query language. A database can be defined as a collection of tables. SQL gets its framework from a table. This language makes it possible to communicate with databases. This programming language is used to retrieve, store, and build data from systems. With SQL, data professionals can get records from a database and develop some of the most potent insights needed for decision-making. 


  • Makes database communication possible: the programming language helps maintain, protect, access, and design SQL databases. 
  • Declarative language: this language uses declarative programming and describes things done by the program but does not control the workflow. 
  • Usage: SQL is widely used. It is one of the most popular languages, and many RDMS or relational database management systems like SQL server, postgres, oracle, MS Access, and MySQL adopt it. 
  • Easy syntax: SQL syntax is relatively straightforward to learn and understand, even if you don’t have any programming language knowledge. 
  • Commands: SQL supports a wide range of commands
  • Flexibility and scalability: You can add and edit new tables with SQL. It also makes it possible to delete old tables that are not needed anymore. This means it scales down or up to help accommodate datasets the business needs. 
  • Integration with databases that are non-SQL: this programming language uses ODBC driver, a middleware, to connect to different databases such as Salesforce and Oracle. 

Comparison between SQL and Python

In the world of data, these two are very popular. The main difference is that Python is used for data exploration and building applications and is a high-level language. At the same time, SQL communicates with databases and is a high-performance language. As a result, the two languages differ in performance, integration, and ease of use.

Pythons’ performance is much slower when there are extensive computations, while SQL enjoys faster performance for simpler aggregations and queries. When the aggregations and queries are simple, SQL is a better option and performs faster than Python. This is mainly because the schema is already defined and computation happens near data. Python requires data extraction and loading before exploration, which can introduce latency. 

  • Functionality: pythons’ functionality is quite extensive because of integration with many libraries, while SQL has limited functionality. The third-party libraries in SQL are not that extensive, and integration may lead to lock-ins. Pythons have a broader functionality range compared to SQL with the presence of third-party libraries. This applies to API development, exploratory data analysis, and machine learning applications. SQL has limited packages that improve its functionality. 
  • Testing: Python allows extensive integration and unit testing using the pipeline and the coding process. For SQL, testing happens over production, and extensive unit tests don’t exist. 
  • Scalability: Python utilizes global interpreter lock or GIL limiting performance and speed when the needs of the system increase. SQL scales up and down by removing or adding tables to the database.
  • Use: Python has a very easy-to-use syntax. With that said, you would have to grasp multiple concepts, making it quite challenging. On the other hand, SQL can be considered beginner friendly and has few concepts to learn. 
  • Debugging: it is easier to debug with Python, and some breakpoints allow execution to be halted if bugs are encountered. SQL models are split into many files to facilitate debugging. However, executions happen but once, and there are no breakpoints. 
  • Professions/roles: Python holds essential roles for data scientists and contains libraries needed to handle many tasks such as data exploration, wrangling, and manipulation. On the other hand, SQL is ideal for data engineers to help with ETL and data modeling tasks.

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Bottom line

Typically, you should learn SQL first. SQL is a good tool for content retrieval from relational databases. If you compare it to Python, most people find it easier to learn SQL, and with it, you can gain the basics of programming languages. This means learning other languages is made much easier. Data retrieval is important in data manipulation, making SQL knowledge necessary when accessing data needed for Python queries application. 

The language you choose to learn first should be based on your interests and goals. Of course, you can enjoy even more benefits when the two languages are used together. However, learning both languages is unnecessary when pursuing careers in data science or computer science.

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