What do you do if you want to learn new programming languages in data engineering?

Start by identifying the languages commonly used in the field, such as Python, SQL, Scala, Java, and R. Next, leverage online resources like tutorials, documentation, and interactive coding platforms to familiarize yourself with the syntax, features, and best practices of the languages. Engage in hands-on projects or coding challenges to apply what you've learned and reinforce your understanding. Additionally, consider joining online communities or forums dedicated to data engineering to seek guidance, exchange ideas, and collaborate with peers. Finally, stay updated with the latest advancements and trends in the data engineering landscape to ensure you're learning relevant technologies that align with industry demands.

This is the my opinion; Choose your learning path based on your goals in data engineering. Python is a versatile choice for many tasks, while SQL unlocks relational databases. For big data, consider Java or Scala, and for statistics and visualization, R might be the way to go. Regardless of your starting point, solidify your programming basics with Python (a beginner-friendly language) and online resources. Then, explore structured online courses with projects to deepen your data engineering knowledge. Actively participate in online forums and communities for data engineers. This is a great way to ask questions, learn from experienced professionals, and stay updated on the latest trends and tools. Finally, an ounce of passion helps.

I started my journey in data with Python and R. I learnt SQL when I needed to work with databases and datalakes. I learnt Scala when I needed to use Spark for big data transformations. Lessons learnt so far: - Don't fall prey to the golden hammer bias. Not every problem is a nail. Learn to use the right tool for your specific problem. - You strengthen a muscle when you use it repeatedly. So don't learn a language, tool or product in the hope it'll be useful in the future. - Learning is hard and it is okay to suck at what you're learning. Be persistent. Keep learning!

Regularly explore new programming languages through online tutorials, read documentation, and participate in coding communities like GitHub. I like to attend workshops, conferences, and webinars focused on emerging languages and technologies. There are lots of virtual webinars and demo days to attend. LinkedIn is a great source for that as well.

languages relevant to your goals and the technologies you want to work with, utilize online resources such as tutorials, documentation, and interactive coding platforms to familiarize yourself with the syntax and features of the new languages, practice coding regularly, working on small projects or challeges to reinforce your understanding. Additionally, consider joining online communities or forums where you can seek advice, collaborate with others, and learn from their experiences. Engage in real-world projects or contribute to open-source projects to gain practical experience and deepen your expertise. Continuous learning and hands-on practice are essential for mastering new programming languages in the field of data engineering

Studying programming isn't an easy task, but before choosing which programming language to learn, I would advise studying the basic concepts of programming, such as data structures, for example... Once you have a solid foundation in programming, learning a new language becomes trivial, as they both follow a similar logic. After that, to choose a programming language, research the market you wish to enter and the primary programming languages used by companies in that sector, and then focus on learning one of them.

Learning new programming languages in data engineering can greatly expand your skill set and open up new opportunities for solving problems and building systems. Identify Your Goals:Understand why you want to learn a new programming language in the context of data engineering. Are you looking to improve your job prospects, work on a specific project, or explore new technologies? Research Industry Trends:Look into current trends and emerging technologies in the DE field. Pay attention to languages that are gaining popularity or being adopted by industry leaders. Assess Job Market Demand:Investigate job listings and requirements for data engineering roles. Determine which programming languages are in demand and align with your career goals.

I find it easily to pick up a new skill or language for data engineering when I can think about the type of impact it would have to the company I work for. Would I be able to use this new tool to more efficiently complete some of the work I've done before? If so, I would try out that new language or technology for a go and that way I don't have to be learning 'on my own time', instead I'm learning for the purpose of building things for the company I'm working for.

Evalua el lenguaje de programación con los siguientes puntos: > Rendimiento y funcionalidades. > Curva de aprendizaje > Documentación y soporte de la comunidad > Casos de uso populares > Paquete y marcos de trabajo populares. > Compatibilidad e interoperabilidad con otros lenguajes o paquetes. No todo es python, hay casos en los que puede sere Java, Scala, Rust o Go.

Courses (paid or free) are useful when you want somebody else to certify that you know something, but is not always necessary. You can use youtube and podcasts to listen and get into general topics while you do something else, and StackOverflow and GPT (or other LLMs) to ask for punctual things, programming with AI copilots is also very useful to learn practically about some common syntax and patterns used in the language. Anyway, you will not really learn anything valuable unless you practice hands on, so you can start with a pet project or with code katas (google it).

There are infinite courses covering several areas on the internet, and this can be a problem if you don’t have focus and perseverance to thoroughly explore a subject. If your goal is to work with programming languages, for example, you should first specialize in just one programming language and then gradually study other programming languages as needed. Don't be afraid to invest money in studying something that will benefit you professionally. Don't hesitate to use ChatGPT and reuse codes from websites like Stack Overflow. Lastly, technology documentations are becoming increasingly intuitive, so don't be afraid to study the documentations of programming languages.

There are many paid resources, but I'd suggest to first explore the enormous free learning resources on LinkedIn, YouTube and do listen to podcasts. There are employee sponsored and preferred sites like Coursera.

There are plenty of free resources to access when searching for new tools to learn or advance your knowledge in. YouTube tutorials or blogs. I’m mainly a visual learner, so videos and doc references vs podcasts work better for me. From there, you can discuss with your employer whether or not this tool is beneficial to your team and/or company which can lead to approval of seminars, conferences, demos, etc.

The moments when I most improved my skills were through practice. Therefore, try proposing an optimization of an existing code in your work. This way, you'll be motivated to study and emprove your abilities.

Let say you are learning python and want to test your knowledge ,Pick a small usecase and try to automate it via python .It will help you to scale yourself and also build confidence. Next steps pick the same usecase and try to build optimize solutions using best practice . There is a difference between just writing a piece of code vs writing optimize code. The first piece of code will give confidence and second piece will give hands on experience.

Learning data engineering is all about hands-on experience and consistency. I recommend starting with small projects to grasp fundamental concepts before moving on to more complex tasks. Begin by getting comfortable with essential tools like SQL, Python, and ETL tools. Then, dive into projects like data cleaning, simple ETL pipelines, or basic data visualizations. As you gain confidence, gradually increase project complexity. Don't forget to apply your skills in real-world scenarios, whether through personal projects, open-source contributions, or volunteering to take on new tasks on the job. Practicing regularly and applying your knowledge in practical settings is key!

To keep up with trends in data engineering, regularly explore new programming languages by engaging in online tutorials, reading documentation, and participating in coding communities like GitHub or Stack Overflow. Attend workshops, conferences, and webinars focused on emerging languages and technologies. Experiment with small projects to apply theoretical knowledge practically. Collaborate with peers to share insights and gain exposure to diverse perspectives on language usage in data engineering contexts.

Well this is a True Story, Being a Veteran in the Data Engineering Field, On my Recent Venture My Client Mandate was to develop all our Data Engineering Pipelines in Java. Not belonging from Java Background poses a challenge for me to implement my solution in Java. But I always keep 1 thought in my mind: "Tough Times doesn't last but Tough People do." I took up the task, I had help from colleagues and with flying colors, I was able to deliver Robust, Efficient Data Engineering Dataflow Pipeline with Java 17 SDK using Apache Beam. I am thankful to all the people who have helped me in my journey.

Always, when switching from one language to other, do a comparison analysis of features offered between the two. Where possible, understand the architecture and design in details and build use cases for use of different languages. Not all scenarios work for the same language.

In my years working as a Software Engineer I learned that a programming language is a tool, and every time you start learning a new stack you should do it with an open mind, they are different and have different proposals to solve different problems. If you are starting in any computer science world, learn SQL and another one non-database language and focus on these two things.