How can you adapt to new programming languages as a data engineer?

Before you start learning a new programming language, make sure you understand basic programming concepts like variables, data types, control structures, and functions. Many of these principles apply to different languages. If you're already familiar with one programming language, try connecting its concepts to those in the new language you want to learn. This can make it easier to grasp the new language. Additionally, there are plenty of sources such as blogs, videos, and books available to help you gain basic knowledge.

Para se adaptar a qualquer linguagem de programação, é fundamental ter uma base sólida em lógica de programação. Compreender os princípios da lógica facilita o aprendizado de qualquer linguagem, pois a lógica básica permanece a mesma. Com uma boa base em lógica e prática contínua, você estará preparado para enfrentar desafios tanto na engenharia de dados quanto no desenvolvimento de software.

New programming language and syntax will be easy to learn if you have a solid base in one programming language and a logical approach to its constructs. Focus on the logical constructs rather than the syntax. Using the problem-solving phase before the implementation phase helps to learn a new language more easily.

As a data engineer, it's crucial to have a strong grasp of at least one object-oriented programming language. You should also be able to identify how a programming language can assist you in solving problems. I can recount several instances where I had to switch between Python, Java, Scala, SQL, Lua, and so on. Once you have a clear understanding of how you plan to use your newfound knowledge, learning and experimenting with data can be quite fascinating.

A while ago I started learning Go language. Then I realized learning a new language isn't about syntax. Once you know once syntax you can easily learn others. What needs to be learned is that languages strengths and weaknesses. What you can do with that language, which benefits that language brings. One core example from my experience, I was using shared variables and locks to solve my concurrency issues instead of using channels. I knew what channels were but apparently i wasn't aware why they are in the Go. Once I get the gist of it the way I view the Go language changed widely. My suggestion is that try to understand the way of writing code in the language you want to learn. Do not bring your old habits to new language.

Always begin with a simple project to understand the basic structures of the language, Like you can start with basic ETL process which involves extracting data from a CSV file, applying simple transformations and load data into target database. Focus on handling missing values, removing duplicates and standardizing formats, these will help you get familiar with the language data manipulation capabilities. Utilize online platforms like Kaggle, community/developer editions to practice on open source datasets then participate coding challenges on LeetCode or HackerRank to work on more challenging scenarios. The key is to start small, gradually increasing the complexity to become more comfortable with the new language.

Try drawing connections between the ideas of the programming language you already know and the new one you wish to learn. This may facilitate learning the new language. In addition, there are a ton of resources accessible to assist you in learning the fundamentals, including blogs, videos, and books.

If you're already familiar with one programming language, try connecting its concepts to those in the new language you want to learn. This can make it easier to grasp the new language. Additionally, there are plenty of sources such as blogs, videos, and books available to help you gain basic knowledge.

Choosing a project that matches your learning goals is essential. For example, if you're shifting to Python for data engineering, consider a project focused on data manipulation, cleansing, and analysis. This might involve creating a basic ETL (Extract, Transform, Load) pipeline using Python libraries such as Pandas or NumPy.

This is a sure short way to be successful in learning a new language and specially the concepts and data structures involved in the new language. Online platforms will help you practice and apply these conceptual principles to solve different problems. Projects will help you establish a slight knack for how the programming language can be connected with other components involved in the entire framework of solving a problem.

I typically begin by exploring the GitHub repository within my organization. I take the time to examine the code contributions made by others, seeking to understand their coding methodologies. This process provides valuable insights into the best practices and optimization techniques specific to the language used.

1. Review other developer codes and try to learn from their code. 2. Refactoring code of myself and the others while doing my task 3. Learn how to write code in the best way

If talking about adopting new programming language is like you know how to make chicken role and want to learn how to make vegetable role. First thing first you have to adopt syntax like how to write conditional statement, prints, create function, file handling etc. But if you don't know any programming language and want to learn one programming from scratch then you have to learn concepts like how loops works, why we use conditional statement and why we create function along with the syntax of programming language. Suggestion: Start from python its very high level and easy to graps and can be used in multiple fields like Web, AI, Data, Blockchain.

Of course, this is the best way to learn. If you are working already in some company try to review the PR’s attentively. Sometimes you get to know different ideas for a same problem from others code and many time you see way to improve this helps the both way. Either you’ll learn new stuff or you’ll help other to know the better way of doing . I believe this way helps both the parties learn new stuff and grow the knowledge base. I personally do it :)

Siempre es bueno leer el código de otra persona para aprender algo nuevo, a su vez recibir feedback pero lo que más me a resultado útil es hacer pruebas unitarias

Since it is a new programming language, there are chances that code executed in first attempt might not be an ideal one from modularity, maintenance, and performance perspective. So until we have specific software or tools available for code design, build, format, test, debug and deployment, code agnostic tools like Git will be quite useful. As language is adapted more, we might be able to leverage them to write crisp and clean code. Until then, developer will have to rely on their new learning and keep updating the code. Performance improvement is one of the area which always require multiple code iterations and testing to identify best possible code for given scenario.

Check your code every few days. You might find better ways to write it. This helps you get better and make your code work smoother.

Regularly review and refactor your code to enhance coding style, readability, performance, and overall quality. Utilize tools like linters, formatters, and code analyzers to identify and correct errors, bugs, or inefficiencies. Adopt version control systems to track changes and facilitate collaboration. Testing and debugging tools are valuable for ensuring the reliability of your code. Documentation, comments, and annotations play a crucial role in explaining your code to yourself and others. Maintain clear and concise documentation to make your codebase more understandable and maintainable. This practice is essential for a smooth transition when working with a new programming language

It is also important to learn how to properly test code in that language, which may involve writing unit tests to validate functionality in isolation, integration tests to check components working together, and end-to-end tests that run an entire workflow. Understand language-specific testing frameworks and best practices around setting up test environments, mocking dependencies, and automating regression testing. The ability to comprehensively test code allows you to detect and resolve issues early and gives confidence that changes will not break existing functionality.

One way to learn how to write better code is through code reviews. Assuming you are in an organization where others can review your code, it is highly recommended to have your code reviewed before it goes into a production setting. Reviewers which know the codebase or organization where you're working in better than yourself, can give tips on code style, testing or patterns you might not be able to find in documentation. If a robust review process does not exist, it might be a good idea to see if implementing pull/merge requests with code reviews can help your organization. In any case ask others for feedback to learn how to improve your code.

Continual learning and exploration are crucial in the dynamic field of programming. Embrace the opportunity to delve into new features and technologies, as this not only broadens your skill set but also keeps you updated with industry advancements. When evaluating your existing codebase, consider replacing features with newer alternatives if they promise improved readability and performance. This proactive approach ensures that your projects remain efficient and aligned with evolving best practices. In the ever-evolving landscape of programming, the commitment to learning and adapting is key to staying at the forefront of technological innovation.

Set aside time for daily study, even if it's just reviewing code snippets or reading articles. Try to always find yourself researching and learning new techniques is paramount in data engineering. Things change all the time, so it's important you learn a little every day if you want to be on top of your game. Join webinars and virtual conferences when possible, as they can offer insights into emerging trends and tools. Remember, consistent incremental updates to your knowledge can lead to substantial growth over time!

Let me turn the question on its head. Do you need to learn a new language? What will it bring? What will it cost to develop, and maintain the new infra and product? In short, what are going to be the gains & costs during the life of the project? There are many great initiatives, and some of them do gain enough traction and transform the industry. In other cases, classics are still going strong (SQL, anyone?) So, by all means, study, learn, adapt. But from risk-aware and cost-aware perspective.

Pay attention to language-specific conventions and best practices that evolve over time - these reflect changing real-world usage and expert perspectives on writing high-quality code. Absorb design patterns and idioms gaining traction. Use linting tools that can automatically flag deviations from the latest quality guidelines related to naming, formatting, complexity, etc. Such vigilance ensures your skills stay current and your style continues maturing closer to industry norms.

In the ever-changing world of programming, lifelong learning and experimentation are essential. Seize the chance to experiment with new features and technologies; this will help you stay current with industry developments and expand your skill set. If newer alternatives offer better readability and efficiency, think about replacing features with them while assessing your current codebase. By taking a proactive stance, you can make sure that your projects stay productive and compliant with changing industry standards. Maintaining technological innovation at the forefront of programming requires a dedication to studying and adapting to the field's constant change.

Learning hardware, regardless of the cloud being almost ubiquitous in modern data engineering. This opens up how the programming language you’re learning interacts with your machine and will therefore facilitate you to use it optimally for your case. You’ll understand WHY static typing is so important in Data Engineering regardless of the fact that python is a dynamically typed language and then implement it. You’ll be able to partition datasets and run transformations in parallel without maxing out on memory or even worse. Making network requests synchronously on a single thread and waiting years for your program to finish collecting data 😭. Learn what’s in your computer. It’ll make learning languages for DE easier!

If the new programming language you are learning is already used in the company you work for, I also suggest spending some time looking at the code reviews done by other team members (open source projects work too). Try to find changes that are closer to the level you are comfortable with while still a bit challenging and new. I have personally often done this to also adapt to new projects. It gives you good insight into how the language is used in the real world, as well as common best practices and mistakes.

Programming languages evolve over time. Stay updated on the latest language features, libraries, and best practices through blogs, newsletters, and official announcements. Follow key influencers and organizations related to the language.

Your brain's a garden, not a museum. Knowledge blooms with constant tending, not dusty displays. Read, workshop, listen – water those seeds. Practice, refine – prune those skills. No grand gestures, just daily sprinkles. Fifteen minutes of sunshine, and your learning habit sprouts. Tiny steps, big impact. Tend your garden, reap the rewards. Every. Single. Day.