Python is a popular programming language used by tech giants like Google and Facebook. You’ll find it mentioned whenever you read about data engineering. However, beginners still often wonder how much Python is used in data engineering or if it is used at all.
Data engineers use Python extensively. It has become the standard language for data science and data engineering. Python libraries like Pandas and NumPy are extremely useful in manipulating data and building data pipelines. This makes Python a must-know language for all aspiring data engineers.
In this article, we’ll discuss how data engineers use Python and what libraries they use. We’ll also look at a few other programming languages that data engineers use every day.
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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How Does a Data Engineer Use Python?
Python is a well-known and widely used programming language, especially when it comes to data engineering. Most job listings have Python as a requirement because it’s the most reliable language for data engineering tasks.
Data engineering is a broad field, and its exact job responsibilities vary greatly depending on the company. However, there are some common tasks that a data engineer performs every day. They use Python and its libraries for most of these tasks.
This is because Python is very versatile. It is a scalable and straightforward language that is also easy to learn. Here are some of the things data engineers use it for:
- Cleaning databases by removing data that are not required for analysis anymore
- Querying a database using SQL and Pandas library
- Coding classes and functions to query remote APIs
- Using Apache Airflow to code ETL (Extract, Transfer, Load) frameworks
- Building and maintaining robust data pipelines to gather data from different sources
- Manipulating small and large datasets using libraries like Pandas and PySpark
- Helping their team members figure out the best approach to using Python and its libraries
These are just some of the things that data engineers accomplish using Python. As we’ve said, Python is a versatile language that can be used for achieving a variety of tasks.
Many big companies also use Python for their projects. For example, Google uses Python, Java, and Goland for their dozens of online services. Facebook, Instagram, Quora, and Spotify have also adopted Python in their technology stack.
What Makes Python Suitable for Data Engineering?
Now, you know how data engineers use Python, but there are other programming languages as well. So, why do data engineers prefer Python? Well, here are four reasons why Python is the perfect language for data engineering and data science:
Extensive Libraries
Python has lots of frameworks and libraries ready to be used. These pre-written codes help you perform specific tasks faster and more efficiently. Python has more libraries for data science than any other programming language, making it perfect for data engineering.
For example, PySpark lets us process data using SQL and read data from different sources using its API. Pandas is another library used to load data remotely and clean, manipulate, reshape, and combine it.
Large User Community
According to the Development Survey 2020 by Stack Overflow, Python is the fourth most popular programming language in the world. It first came about in 1991, and in these 29 years, a vast and helpful community has grown around it.
Any problem you face while using Python has probably been encountered by somebody else in the past. You’ll find hundreds of YouTube videos, online courses, blog articles, and forums to guide you. This large user community is also the reason why Python has many operational frameworks and libraries.
Scalability
Python is simple yet flexible. It can easily be scaled to accommodate projects of any size. With Python, you don’t have to worry about memory leaks or work for compilation. Things happen quickly, and you can see the changes you’ve made. Python can also be combined with other programming languages to achieve desired results.
In Python, there are many ways to solve the same issue. This decreases errors as developers and engineers can solve problems in the simplest ways and work however they feel comfortable.
Easy to Learn
Python is a simple language with concise and human-readable code. It’s one of the easiest programming languages to learn as a beginner. This is because it allows you to achieve the same result by writing fewer lines of code than other languages.
Any English-speaking person can roughly comprehend what is happening just by looking at Python code. The best part is that even though Python is simple, the functional possibilities are endless.
Are There Any Alternatives to Python for Data Engineering?
We’ve seen how Python is the go-to language for data science and data engineering. It is very popular, and most jobs require proficiency in it, but there are also a few other languages prevalent in the big data community. Although they are not a replacement for Python, they can work alongside it to increase your data engineering tasks’ efficiency.
Here are three data engineering languages other than Python:
R
R is not very popular among data engineers, although you can achieve many data engineering tasks using R. Python has several advantages over R. It performs better in repetitive tasks and data manipulation. After Python, Scala is the preferred choice for building data pipelines and models.
However, you can still use R to build small data engineering applications. Just as you can process small datasets using Python with Pandas, it is possible to do the same using R with dplyr. However, modern data engineering teams prefer using Python’s libraries like PySpark and Pandas.
Scala
Scala is an extension of Java and runs on the Java Virtual Machine (JVM), making it compatible with many Java libraries. Just as Python is popular among big companies, Scala is also used by tech giants for building scalable applications and data engineering infrastructure. Companies like Netflix, Twitter, LinkedIn, Tumblr, and Airbnb use this language.
Spark, a fast, open-source engine for processing big data, is written in Scala. It’s recommended to know Scala if you’re working on a Spark project and want to get the most out of the framework. Some Spark APIs, like GraphX, are only available in Scala.
Although Python is more popular and gets more traction, Scala is also suitable for data engineering. It has a powerful type system and supports all data-related tasks. Many data engineers know both languages as they work nicely as a pair.
Java
Java is the fourth most-trending tech skill for data engineers, according to Cloud Academy. This is because programs essential for big data engineering, like Apache Hive and Apache Hadoop, are written in Java. However, Java is still not a strict requirement for data engineering because Python, too, is capable of handling everything.
Some companies specifically look for data engineers experienced in Java because they have existing data pipelines and in that language. Usually, big companies use a combination of languages and technologies for their projects. So, knowing Java undoubtedly makes you a more competent data engineer and gives a significant boost to your career.
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.
Conclusion
Data engineers use various tools and technologies. Getting a job as a data engineer requires you to master at least one programming language. Most professionals use Python because of its simplicity, scalability, and extensive frameworks.
Alternatives of Python include R, Scala, and Java. These languages don’t entirely replace Python because most companies have existing data pipelines in Python. However, many data engineers learn these languages to use them alongside Python. Scala is preferred over Python for utilizing Apache Spark.
Python is a must-know programming language for data engineers. They use it every day at various stages in the data process.
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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