SQL vs Python: which to use for data analysis

When I first got started with data science, I remember trying to figure out how to complete a certain task and finding answers on Stack Overflow (I’m dating myself) that pointed to either SQL or Python. With some software experience from school but no formal training in software development, it left me confused: should I be using SQL or Python? In the end it quickly became clear to me that to do any sort of data analysis, I had to leverage both, but each had its pros and cons. And although there are certain tasks that can be done in both languages, there are certain things that can only be done in either one.

In this post we’ll talk about what Python and SQL are, when you should use one or the other, and which to get started with if you’re just getting started.

For a walk-through with examples, check out our video:

What are Python and SQL?

Before diving into the nuances of SQL vs Python, let’s set the stage by explaining what these two technologies are at a high level.

Python: A quick primer

Python is a high-level, interpreted programming language known for its readability and versatility. Created by Guido van Rossum and first released in 1991, Python emphasizes code clarity and a syntax that is closer to natural language than many other programming languages. This makes Python one of the most popular languages not just for data science but also for web development, automation, scripting, artificial intelligence, and beyond. Python has also become extremely popular in the data analysis and data science communities thanks to packages such as Pandas, NumPy and scikit-learn. It has also exploded in popularity more recently because it’s one of the coding languages that Large Language Models (LLMs) excel at. 

Some notable characteristics of Python:

• Simplicity and readability: Python’s syntax was designed to be clean and straightforward, making it an excellent choice for beginners.
• Extensive standard library: Python has a large standard library (known as the “batteries-included” approach), meaning a wide range of functionality is available by default.
• Vibrant ecosystem of packages: Powerful libraries like NumPy, Pandas, and Matplotlib make data analysis and visualization more efficient. Frameworks like Django and Flask power web development, while machine learning libraries like TensorFlow and scikit-learn are industry standards.
• Versatility: You can build web applications, analyze data, automate tasks, create APIs, run serverless applications, and much more with Python.

SQL: A quick primer

SQL (Structured Query Language) is a domain-specific language used primarily for managing data in relational database management systems (RDBMS). Created in the early 1970s and standardized by the American National Standards Institute (ANSI), SQL is older than most languages still in common use today—including Python. Despite its age, SQL remains one of the most widely used languages in the tech world and is essential for anyone working with relational databases.

Any individual doing any sort of data analysis must know at least the basics of SQL.

Some of SQL’s core features include:

• Data retrieval: SQL excels at quickly retrieving specific subsets of data from large databases using SELECT queries with powerful filtering, grouping, and sorting options.
• Data manipulation: You can insert, update, and delete data with minimal fuss—important for maintaining and transforming data.
• Schema definition: SQL lets you define tables, set constraints, and create relationships between data tables. This is crucial for building robust data structures.
• Transaction control: SQL supports complex transactions to ensure data integrity. It can roll back changes if something goes wrong, maintaining a reliable record.

When to use SQL vs Python?

Now that we have a high-level understanding of both languages, let’s explore SQL vs Python from a use-case perspective.

Use cases for SQL

1. Direct database interactions: If your task is to store or retrieve data from a relational database, SQL is the most straightforward and efficient way to do it.
2. Complex queries: SQL shines in cases where you need to join multiple tables, aggregate data, or perform advanced filtering. Operations like GROUP BY, JOIN, and subqueries are extremely powerful.
3. Data integrity: When data needs to be managed with strict rules, constraints, or relationships between tables, SQL is the backbone that enforces these relationships.
4. Reporting and dashboards: Many analytics or Business Intelligence (BI) tools rely heavily on SQL queries. If your job revolves around building dashboards and generating insights, SQL is indispensable.

Bottom line: SQL is great for pulling data as tables and slicing and dicing that data. You can think of it as a coding language version of a spreadsheet.

Use cases for Python

1. Data manipulation beyond database constraints: While SQL can handle data manipulation within the database, Python, especially with libraries like Pandas, can perform more complex data transformations, merges, and computations that might be cumbersome or impossible in raw SQL.
2. Automation and scripting: Python is a general-purpose programming language, so you can use it to automate anything from file manipulation to pulling data from APIs and orchestrating complex workflows.
3. Machine learning and advanced analytics: Python’s ecosystem includes libraries for machine learning (TensorFlow, PyTorch, scikit-learn) and data analysis (NumPy, Pandas). If you’re building predictive models or employing deep learning, Python is crucial.
4. Web development: Frameworks like Django or Flask allow you to create entire web applications in Python, often with built-in support for connecting to databases (which might, in turn, use SQL under the hood).

Bottom line: Connecting to live data sources isn’t as straightforward in Python and although you can use it to slice and dice data, it really shines when doing advanced data science and machine learning, data visualization or more advanced data cleaning and wrangling.

When SQL alone is enough

There are some scenarios where you can rely solely on SQL without needing to integrate Python:

• Straightforward data analysis: If all your data is structured in relational databases and your analysis doesn’t require advanced computations, SQL queries might be all you need.
• Simple or pre-defined reports: Many organizations rely on pre-built SQL queries for generating the same reports on a regular schedule. This is a typical scenario in business reporting or data warehousing.

When Python alone is enough

Likewise, there are times when Python alone can handle the entire data pipeline:

• ETL (Extract, Transform, Load) without a traditional RDBMS: If you’re pulling data from APIs, flat files, or NoSQL databases, you can build your entire data pipeline in Python.
• Machine learning prototypes: You might store data in files or a NoSQL store and use Python’s data-manipulation libraries to train and evaluate models without relying on a relational database.
• Automation: For tasks like sending emails, moving files around, scheduling scripts, or building command-line tools, Python is an excellent choice.

However, in real-world data projects, it’s quite common to combine SQL and Python to get the best of both worlds. We cover this topic in more detail in another post and cover some practical examples of how to use both in tandem.

A note on Fabi.ai: Fabi.ai is a collaborative AI data analysis platform that lets users alternate between SQL and Python for maximum efficiency. This means that you can quickly retrieve and aggregate data from your data warehouse, then immediately manipulate that data as a Python pandas DataFrame in the same report. If you’re curious to see this in action, you can try it out for free and get started in less than 2 minutes.

Just getting started: Should you learn Python or SQL?

If you’re just entering the field of data analytics, data science, or software development, you might be wondering which one you should learn first.

Data analysis vs data science vs software development

If you take anything away from this: if you’re looking into SQL and Python for data analysis purposes, SQL is a must-have. Although most data jobs will require both SQL and Python SQL, nearly every data job will require SQL skills, but the inverse isn’t always the case.

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Let’s break it down:

1. If you plan to focus on data analytics:some text
   • SQL is a must-learn for data analysts, BI professionals, or anyone working heavily with relational databases. You will see this as a skill that you must master in any job description.
   • Python comes in handy if you plan to perform more advanced analytics, automate tasks, or get into data science. In general, for analysts, mastering SQL first is often recommended, because so much of the “data analytics” job revolves around querying databases. Once you’re comfortable with SQL, adding Python to your skill set will open a lot of doors.
2. If you’re aiming for data science or machine learning:some text
   • Python is non-negotiable. It’s the dominant language in the data science community, thanks to Pandas, NumPy, scikit-learn, TensorFlow, and other libraries.
   • SQL is still a key skill. You’ll almost certainly need to query data from some kind of database at some point. However, if your main focus is building predictive models or doing statistical analysis, Python is where you’ll be spending most of your time.
3. If you’re a software developer:some text
   • Python is an excellent general-purpose programming language for web development, scripting, and automation.
   • SQL is still valuable because nearly all applications store or retrieve data from some type of database. While you might not write a ton of raw SQL if you’re using an ORM, understanding SQL is critical for debugging, optimizing, and designing schemas.

Job market considerations

From a job market perspective, both SQL and Python are in high demand. According to various tech job boards and reports, you’ll find that:

• SQL is one of the most requested skills for data-related roles.
• Python is one of the top 5 programming languages globally and continues to see growing adoption.

For data-specific roles—whether in analytics, engineering, or science—employers frequently list both Python and SQL as required or strongly recommended. If you’re in a position to do so, learning both at some level of proficiency will likely give you a significant advantage.

Learning curve

• SQL: On one hand, SQL can be simpler to learn conceptually, because it’s a declarative language that reads similarly to English (“SELECT * FROM table WHERE …”). You can often get started quickly by writing basic queries, and you’ll see immediate results.
• Python: On the other hand, Python is also known for its beginner-friendliness. However, because it is a full-fledged programming language, you might need to learn additional programming fundamentals (e.g., loops, functions, classes) to unlock its full potential. Learning Python also means that you’ll need to learn how to work in the command line, how to use version control tools such as GitHub and how to work in an IDE such as VSCode.

Recommended learning path

1. Start with SQL: If your main task is to query existing databases or build reports, dive into SQL first. You can become proficient in writing queries, understanding schemas, and optimizing queries in a relatively short amount of time.
2. Then pick up Python: Once you’re comfortable with relational data and queries, layer on Python. You can continue to use SQL for data retrieval, but Python will let you explore advanced analytics, automation, and machine learning.
3. Blend the two: Ultimately, the modern data professional often uses both. Practice building small projects that integrate Python and SQL together, like a mini data pipeline that pulls data from a database, processes it in Python, and outputs results back into the database.

Resources to get started

Here’s a list of some well-reviewed resources to help get you started:

• SQLBolt (Free)
• FreeCodeCamp (Free)
• Coursera for SQL or Python (Paid)
• Datacamp for SQL or Python (Paid)

Use SQL for querying and manipulating data tables, use Python for advanced data analysis and visualization

When it comes to SQL vs Python, it’s important to remember that this is not necessarily an “either-or” choice. Each language excels at different tasks, and in the day-to-day life of a data professional or software developer, they often go hand-in-hand:

• SQL is the best tool for interacting with relational databases, running complex queries, and enforcing data integrity. If you’re doing data analysis, any project will start with SQL.
• Python is the Swiss Army knife of programming languages, ideal for building applications, automating tasks, and performing sophisticated data analysis or machine learning. In the world of data analysis, it’s the go-to for machine learning, data science and data visualization.

If you’re looking to break into data-related fields, you’ll likely need both skills. In many workplaces, SQL is the language everyone uses to retrieve and manage data, while Python is the language used to analyze that data further, automate processes, or build new functionalities.

So, whether you’re at the start of your career or looking to expand your skill set, consider learning both. Start with the technology that best matches your immediate goals—SQL for data querying and database management, or Python for general-purpose scripting and advanced analytics. Then, expand your horizons by learning the other. You’ll be far more versatile, valuable, and marketable once you’re adept in both worlds.

At Fabi.ai, we understand the importance of both for data analysis and we believe that data practitioners should be able to interweave the two seamlessly. This is why in our Smartbooks we offer a mixed-environment so that you don’t have to choose. And with our AI-assistant, getting started has never been easier. Check us out for free here.

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