There’s a tremendous demand for data engineers in today’s time, but becoming one is not that easy. You require at least a year of learning and project building before landing a job as an entry-level data engineer. Aspirants need to learn several data engineering tools and techniques, and they often wonder if coding is one of them.
Data engineering requires strong coding knowledge. Coding is one of the critical skills you have to learn to get your first job as a data engineer. Data engineers are required to code robust data pipelines to transform unstructured data into a useful format for analysis.
This article will discuss precisely how much coding data engineering requires and what data engineers code. Apart from that, we’ll also look at what languages you need to learn to become a professional data engineer.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
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Is Coding Necessary for Data Engineering?
To answer that question, you need to understand what these professionals do. Data engineers essentially act as a bridge between databases and data science teams. They collect, clean, and organize the data so that scientists and analysts can use it to gain new insights. They are also responsible for coming up with solutions for big data handling and storage.
And how do data engineers achieve all this? By using programming languages to build and maintain data pipelines, infrastructure, and databases.
This means coding is at the core of data engineering. In fact, data engineering requires you to have very strong coding skills. This is so that you can use frameworks, libraries, and other tools to their full potential. So, it’s not that you just have to know how to code; you also have to be very good at it.
When hiring data engineers, employers look for past work experience or personal projects that showcase your knowledge of programming and data engineering concepts. This means you won’t be able to get a job if you can’t prove that you’re a competent coder.
But can you somehow become a data engineer without any coding knowledge? No, it is simply not possible. You must possess a set of technical skills for data engineering, such as programming languages, ETL tools, distributed systems, and machine learning algorithms. Other than that, you also need to have soft skills like public speaking, presentation skills, and strong communication skills.
What Programming Languages Do You Need To Learn for Data Engineering?
So now you know that you really can’t do without coding if you want to pursue data engineering. But exactly how much coding do you need to learn? What are the essential programming languages that will help you land a job? Below are the top five programming languages for aspiring data engineers.
Note that the following list is not at all comprehensive; it only covers some programming languages. There are several more tools and techniques you need to learn to become a data engineer. They include ETL technologies, Amazon Web S3, distributed systems, data APIs, ML algorithms, data structures, and NoSQL databases.
Since data science has begun growing in popularity, Python has quickly become the standard language for all things data. It’s used extensively in data science, server-side scripting, and backend systems. If you want to become a data engineer, you must understand the core concepts of Python and have a firm grasp of the language.
The good news is that Python is easy to learn, and there are plenty of resources online to teach you the ins and outs of Python. You don’t have to know everything; it’s enough to know how to use the language to solve different data problems.
When it comes to data engineering, Apache Spark is a widely used analytics engine for big data processing. Cloud Academy reports it to be the third most sought data engineering skill.
After Python, Scala is the recommended language for data engineers because Apache Spark is built with Scala. Since the language runs on Java Virtual Machine (JVM), it is compatible with many Java libraries. Companies like Airbnb and Netflix use Scala for data engineering infrastructure and scalable applications.
You don’t have to learn both Python and Scala. I recommend researching the pros, cons, and uses of both programming languages. Then, you can decide which one you want to learn based on your personal liking.
According to Cloud Academy, Java is the fourth most-trending tech skill for data engineers. Though Java is not a strict requirement for data engineering, it’ll surely give a significant boost to your career. Java is extensively used in data architecture frameworks, and their APIs are mostly designed for this language.
Essentials programs like Apache Hive and Apache Hadoop are written in Java. As a data engineer, knowing this language gives you an advantage because you can better understand the behind-the-scenes of the software you’re using.
R is another Python alternative for data engineering, though it’s more popular among data scientists than data engineers. R is used to set up statistical models and analyze data. It can also be very useful for machine learning applications.
Note that R doesn’t have a lot of use in data engineering. Python and Scala are the preferred programming languages, along with SQL for database management. But that doesn’t mean you can’t perform data engineering tasks with this language. You can process small datasets using R with dplyr just as you would do using Python with pandas.
Database Management Systems
Setting up, querying, and managing database systems is a big part of a data engineer’s job. Therefore, you can’t get away from learning SQL (Structured Query Language). It is the standard language for maintaining and manipulating relational databases. According to Cloud Academy, SQL is the most sought skill for data engineering.
Since SQL is an established language, it won’t be going away anytime soon. As a data engineer, you will be using SQL every day, so it pays to know this language inside out. It’s also essential to know how to model data and work with less structured datasets.
This article is an excellent resource if you want to learn more about SQL and how to learn it for data engineering.
How Hard Is It To Learn To Program for Data Engineering?
After reading about the required programming languages, you’re probably wondering how difficult it is to learn them. Well, if it were a piece of cake, there wouldn’t be a shortage of talented data engineers in today’s time. You will need to work hard for months to learn the basics of programming and database management.
However, there are various free and paid resources on the internet to make programming fun. Sites like FreeCodeCamp and Coursera offer comprehensive free courses on most of the languages listed above.
Overall, it can take you around 12 to 15 months to learn data engineering and land your first job. I’ve written a full blog post discussing how hard it is to become a data engineer. You can read it here: Is Data Engineering Easy? Or, Is It Hard?
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
More and more people want to learn data engineering as it’s an incredible career with excellent growth opportunities. However, data engineering requires strong coding skills. You need to learn programming languages such as Python, Java, R, Scala, and SQL. Although you don’t have to know everything about these languages, it’s essential to know enough to solve data-related problems using them.
Programming is an integral part of data engineering. SQL and Python are a requirement in almost every job listing online. So it’s crucial to get comfortable with these languages and learn how to manipulate data using them.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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