Introduction
“SQL or Python – which one should I learn first?” is a common question for aspiring data engineers. Both SQL and Python are indispensable tools in the data engineering toolbox, but they serve different purposes. SQL (Structured Query Language) is the go-to language for querying and manipulating relational databases, while Python is a general-purpose programming language widely used for building data pipelines, automation, and analytics. If you’re aiming for a career in data engineering, you’ll eventually want to be proficient in both. But when you’re just starting out, it’s natural to wonder where to focus first.
The truth is, there is no one-size-fits-all answer – it depends on your background and career goals. Industry data shows that both SQL and Python are highly in-demand for data engineers (about 79% of data engineering jobs list SQL and 74% list Python as required skills). Employers expect you to have at least a working knowledge of both. That said, the order in which you learn them can affect how quickly you can contribute in a job. This article will break down the roles of SQL and Python in data engineering, the pros and cons of learning each first, and how to decide your learning path.
By the end, you’ll have a clearer idea of whether to tackle SQL or Python first on your journey. We’ll also share insights from experienced engineers and practical tips for mastering both. Whether you’re a college student or an IT professional transitioning into data engineering, this guide will help you create an efficient learning roadmap. (In fact, Refonte Learning – which offers data engineering training programs – finds that the “SQL vs. Python first” debate really comes down to understanding what each language is used for.) Let’s dive in and demystify the choice.
Data Engineering 101: SQL and Python in Your Toolkit
Before deciding which to learn first, it helps to understand how SQL and Python are used in data engineering. Data engineers are responsible for building systems that collect, store, and process large volumes of data. In doing so, they work extensively with databases and data pipelines.
SQL is fundamental for any work involving databases or data warehouses. Data engineers use SQL to create and manage database schemas, extract data with queries, transform it using joins and aggregations, and load results into target tables (the “T” in ETL stands for Transform, and often that transformation logic is written in SQL). If you’re dealing with structured data stored in relational databases or cloud data warehouses (like Snowflake, Redshift, or BigQuery), SQL is your primary tool to interact with that data. It’s a declarative language – you specify what data you want, and the database engine figures out how to get it. SQL’s syntax is relatively straightforward (e.g. using SELECT and WHERE clauses), and it’s designed specifically for working with sets of data.
Python, on the other hand, is a versatile programming language that can do much more than SQL alone. In data engineering, Python is commonly used to write scripts or applications that move and process data outside of databases. For example, you might write a Python script to fetch data from an API, cleanse or transform that data, then save it to a database. Unlike SQL, Python is procedural – you explicitly code the steps to manipulate data – which gives you a lot of flexibility to implement complex logic. Python also has a rich ecosystem of libraries (like pandas for data manipulation, or SQLAlchemy for connecting to databases) that make a data engineer’s life easier when dealing with data that isn’t neatly stored in a single database.
In summary, a data engineer uses SQL for working within databases (querying and transforming structured data) and Python for building programs and pipelines that integrate various data sources and perform processing outside of databases. Think of SQL as the language for interacting inside the data warehouse, and Python as the language for interacting with everything around it (and glueing different systems together). Both are powerful and commonly used – the question is, which should you master first?
The Case for Learning SQL First
Many experts recommend learning SQL before Python if you’re pursuing data engineering. There are a few reasons why starting with SQL can be advantageous:
Simplicity and Beginner-Friendliness: SQL is generally considered easier to learn than Python. Its syntax is closer to plain English (e.g. “SELECT * FROM table WHERE condition”), and it has a narrower scope – it’s only used for working with databases. You can grasp the basics of SQL quite quickly, even without a programming background. Learning SQL first can build your confidence as you start working with data.
Immediate Utility in Data Roles: Knowing SQL allows you to contribute to many data projects right away. Entry-level data jobs (like junior data analyst or engineer positions) often involve writing queries or pulling data for reports. If you start a data engineering internship or job, you’re likely to be handed tasks that involve writing or optimizing SQL queries early on. By mastering SQL, you’ll be able to add value quickly in such scenarios. For example, you might be tasked with joining several tables to create a dataset for analysis – a classic SQL task.
Foundation for Data Thinking: SQL teaches you to think in terms of sets and relationships, which is a valuable way of thinking about data. You’ll learn how data is structured in tables and how to use joins to combine data from different tables. These concepts are fundamental for a data engineer. Even as you move to big data tools or NoSQL databases, understanding how to filter, aggregate, and join data remains essential. SQL gives you a strong foundation in dealing with structured data.
Ubiquity in Data Engineering: Virtually every data engineering role requires SQL. Even if a job emphasizes another language or tool, SQL tends to be used whenever relational data is involved. Some data engineers joke that they end up writing SQL even for tasks they initially intended to do in Python, because ultimately the data lives in a database. Starting with SQL ensures you cover this universal requirement first. It’s not unusual to find roles where a majority of the work is done in SQL (one engineer noted that in a data warehouse-centric job he spent 95% of his time on SQL and only 5% on Python).
SQL’s strengths lie in its focus and simplicity. By learning it first, you can gain practical skills that make you productive in many scenarios. For instance, Refonte Learning often encourages beginners in its data engineering program to get comfortable with SQL early on, so they can handle database queries and transformations before diving into full-scale programming. Mastering SQL first gives you a “language of data” that will support everything else you learn later.
One thing to note: because SQL is specialized, you won’t learn broader programming concepts (like loops or functions) until you pick up a language like Python. That’s where considering the other side of the coin comes in.
The Case for Learning Python First
There is also a strong argument for starting with Python, especially if you aim to have a broad skill set or already have some programming background. Here’s why you might choose Python as your first focus:
Versatility and Wider Applications: Python is a full-fledged programming language, so by learning it, you’re not only preparing for data engineering tasks but also gaining skills useful in many areas (automation, web development, data analysis, etc.). If you learn Python first, you’ll become comfortable with programming logic (loops, conditionals, functions) that can be applied to any software project. This versatility can be beneficial early in your career. For example, you could automate parts of a data process with Python scripts, which goes beyond what SQL alone can do.
Essential for Complex Pipelines: In modern data engineering, especially in big data environments, a lot of pipeline and ETL development is done with languages like Python (or Scala). If your goal is to work on data pipelines that involve multiple sources and transformations beyond what SQL can handle, Python is often the tool of choice. By mastering Python first, you quickly become capable of building end-to-end data workflows. For instance, you could write a Python program that reads JSON files, cleans the data, and loads it into a database – covering an entire pipeline by yourself.
Introduction to Software Engineering Practices: Learning Python will expose you to software development concepts that SQL won’t, such as writing modular code, error handling, version control, and testing. These skills become important as you progress to advanced data engineering roles (where you may be developing complex applications, not just queries). Starting with Python means you begin practicing how to structure code and solve problems algorithmically from the get-go. Some data engineering teams view themselves as software engineering teams that work with data. One veteran data engineer shared that most of his projects over 20 years were about 80-90% Python code and only 10-20% SQL – highlighting how code-heavy certain data engineering tasks can be.
Alignment with Data Science and ML: If your interests overlap with data science or machine learning, Python is the gateway. Data engineers often collaborate with data scientists, and Python is the common language there (for libraries like pandas, NumPy, scikit-learn, etc.). By learning Python first, you set yourself up to interface with those domains too. Even if you remain focused on engineering, understanding Python-based ML or analysis code can be a bonus.
Learning Python first means you’ll tackle a broader and at times more challenging learning curve upfront, but it can pay off by making you a more well-rounded technologist. A platform like Refonte Learning might start someone with Python first if they come from a software background, then introduce SQL once they have basic programming down. For a coding-oriented person, that can be an efficient path. Python first can be especially sensible if the data engineering roles you aspire to involve a lot of custom coding or big data processing where SQL alone isn’t enough.
Key Takeaways for Aspiring Data Engineers
You Need Both SQL and Python: A successful data engineering career will require proficiency in both SQL and Python. SQL is essential for database querying and management, while Python is crucial for building data pipelines and handling unstructured data. Plan to learn and continuously improve both skills.
SQL First for Data Focus: If you’re new to coding or primarily interested in data management and analytics, learning SQL first can give you quick wins. It’s easier to learn and immediately applicable to many entry-level tasks (like writing queries, creating reports, or working with data warehouses).
Python First for Broader Skills: If you already have some coding background or aspire to handle more complex engineering tasks, you might start with Python. Python will teach you general programming concepts and allow you to work on a wider range of data engineering tasks early (e.g. automation, integrating APIs, using big data frameworks).
Leverage Both in Projects: Don’t silo SQL and Python. Practice projects that use them together – for example, use Python to fetch or preprocess data, then use SQL to insert that data into a database and analyze it. This will help you understand real-world data engineering workflows and how the two skills complement each other.
Use Structured Learning Resources: Consider guided courses or bootcamps to learn systematically. Refonte Learning offers courses in both SQL and Python as part of its data engineering track, along with hands-on projects. Structured programs ensure you cover important topics (like SQL performance tuning or Python data libraries) and get feedback on your work.
Conclusion
In the debate of SQL vs. Python for data engineering, the winner is ultimately both. These two skills aren’t mutually exclusive – they complement each other. The decision of which to master first should depend on your starting point and career objectives. If you begin with SQL, you’ll gain a strong grasp of working with structured data and can contribute quickly in database-centric tasks. If you begin with Python, you’ll build a broad coding skill set that will serve you in complex data pipeline scenarios. Neither path is wrong, and as long as you commit to learning both in the long run, you’ll be in great shape.
Remember that data engineering is a journey of continuous learning. The core tools may evolve over time, but the foundational ability to work with data effectively will always be key. Stay curious and keep practicing. Whether you write a slick SQL query or an efficient Python script, what matters is delivering reliable, well-designed data pipelines. By building expertise in both SQL and Python – with guidance from resources like Refonte Learning and plenty of practice – you’ll become a versatile data engineer capable of tackling a wide range of challenges. Pick a starting point, dive in, and happy learning!
FAQs about SQL vs. Python for Data Engineering
Q1: Should I learn SQL or Python first for data engineering?
A1: It depends on your background. If you’re completely new to programming, SQL first can be easier and will quickly allow you to work with databases (a big part of data engineering). SQL’s syntax is simple and very useful for any data-related role. If you already have some coding experience or you’re interested in the more software-heavy aspects of data engineering, you might start with Python first. Python lets you build data pipelines and automation. Ultimately, there’s no strict rule – many people learn the basics of both in parallel. The important thing is that you learn both eventually. Choose one to start based on what feels more comfortable or immediately relevant, then tackle the other soon after.
Q2: Is SQL easier to learn than Python?
A2: For most people, SQL is easier to learn than Python. SQL has a concise, English-like syntax and is used for a specific purpose (querying databases), so you can become functional in SQL fairly quickly. Python, being a full programming language, has more concepts to learn (data types, logic, syntax, etc.), which can make it a bit more challenging initially. That said, Python is known for its readable syntax and has a gentle learning curve compared to many languages. Many beginners find that once they are comfortable with SQL, learning Python isn’t too intimidating. Overall, SQL tends to be easier as a first language for those new to coding, while Python might take a little more time to grasp but pays off with its versatility.
Q3: Do data engineers need to know both SQL and Python?
A3: Yes – virtually all data engineering positions expect proficiency in both SQL and Python (or another programming language). SQL is needed to interact with relational databases and perform data transformations within those systems. Python is used to write code that connects systems, automates workflows, and processes data outside of databases. Some junior roles may emphasize one over the other (e.g. a role might involve mostly SQL if it’s heavy on data warehousing, or mostly Python if focused on building pipelines), but having skills in both makes you far more effective and marketable. Surveys of job postings back this up – a huge percentage of data engineer jobs list both SQL and Python as required skills. The good news is SQL and Python don’t conflict; they complement each other. A great data engineer can write efficient SQL queries and also develop solid Python code for data processing. Ensuring you learn both (through courses, projects, etc.) will prepare you well for the job.