You’re a data engineer. How do you know which programming languages to learn?

As a data engineer, you are responsible for designing, building, and maintaining data pipelines that collect, process, and store data from various sources. Data engineering is a fast-growing and dynamic field that requires you to constantly update your skills and learn new technologies. But how do you know which programming languages to learn and master as a data engineer? In this article, we will explore some of the factors that can help you decide which languages to focus on, and some of the most popular and useful languages for data engineering in 2021.

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1 Factors to consider

It can be difficult to determine which programming languages are best for data engineering, as various languages have distinct advantages and drawbacks in different contexts. To narrow down your options, consider the type and scale of data you are dealing with, the tools and frameworks you are using, and the skills and preferences of your team and organization. For instance, if you are dealing with large volumes of structured data, a language optimized for querying and manipulating relational databases, like SQL, may be appropriate. If you are using Apache Spark for distributed data processing, a language compatible with the Spark API, such as Scala, Java, or Python may be beneficial. Additionally, if your team has a strong background in Java or your organization values innovation and experimentation, you may want to use a language that is more versatile and dynamic such as Python or R.

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Jocelyn B.

AI/ML Big Data Engineer | MS Software Engineer
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Consider the needs of your client or the organization. Understand the importance of how the data will be collected, process ie batch streaming for analytics or real time streaming for transactional data. From here the data engineering team can decide what best data warehouse, ETL framework and programming languages associated to it are best to use.

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2 Popular and useful languages

Data engineering involves a variety of programming languages, and some of the most popular and useful ones in 2021 are SQL, Python, and Scala. SQL stands for Structured Query Language and is the de facto standard for querying and manipulating data in relational databases. It is used for tasks such as data extraction, transformation, loading (ETL), data analysis, and data quality checks. Python is a general-purpose, high-level, and interpreted programming language that is known for its readability, simplicity, and versatility. It is used for tasks such as data wrangling, data exploration, data visualization, and data science. Scala is a hybrid, multi-paradigm, and compiled programming language that combines the features of object-oriented and functional programming. It is used for tasks such as distributed data processing, stream processing, and real-time analytics. All three languages are powerful tools for data engineers who want to work with diverse and complex data sources or large-scale high-performance data systems.

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Ashutosh Tripathy

SSE@HCLTech R&D | .py | Ex-KPMG, TCS
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For me, Python's versatility and rich ecosystem make it a top choice. It's like the Swiss Army knife of programming languages. Plus, its libraries like Pandas and NumPy make data manipulation a breeze. But don't overlook other contenders like Java or Scala, especially if you're diving into big data territory with frameworks like Apache Spark. Ultimately, it boils down to what tools you need for the job and what feels most comfortable for you. Just remember, in the world of data engineering, flexibility is key, and Python's got that in spades!

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3 Other languages to consider

Data engineering requires specific languages depending on one's needs and goals. Java is a general-purpose, object-oriented, and compiled programming language that is known for its portability, reliability, and scalability. It is often used for distributed data processing, stream processing, and web development, as it is compatible with many data engineering tools and frameworks. R is a specialized, high-level, and interpreted programming language designed for statistical computing and graphics. It has a rich ecosystem of libraries and frameworks that support data engineering projects. C# is a general-purpose, object-oriented, and compiled programming language developed by Microsoft. It is often used for web development, cloud computing, and mobile development. Each of these languages has its own advantages; Java is mature, robust, and secure; R is powerful, flexible, and domain-specific; C# is modern, elegant, and interoperable.

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4 How to learn and practice

If you are looking to learn and master a programming language as a data engineer, it is important to find the best ways to do so. To start, you should gain a solid foundation of the syntax, data types, operators, control structures, and functions. You can use online courses, books, tutorials, or videos to learn the basics and practice with simple exercises and challenges. Additionally, it is beneficial to use the language to solve real-world problems and build real-world projects. Platforms such as Kaggle, HackerRank, or DataCamp can provide data engineering problems and projects that match your skill level and interests. Furthermore, you can learn from other data engineers who have experience and expertise in using the language through online communities such as Stack Overflow, Reddit, or Quora. Lastly, staying updated with the latest trends and developments in data engineering and programming languages can be achieved through online resources such as blogs, podcasts, or newsletters.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You’re a data engineer. How do you know which programming languages to learn?

1

2

3

4

5

1 Factors to consider

2 Popular and useful languages

3 Other languages to consider

4 How to learn and practice

5 Here’s what else to consider

Data Engineering

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You’re a data engineer. How do you know which programming languages to learn?

1

2

3

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5

1 Factors to consider

2 Popular and useful languages

3 Other languages to consider

4 How to learn and practice

5 Here’s what else to consider

Data Engineering

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