What do you do if you need guidance in a specific data engineering domain?

In my view, a few steps to this often help, such as: Clarify the exact area of data engineering where you need guidance, Find a mentor with experience in the specific data engineering domain you are interested in, Apply your knowledge working on real-world projects, try open source tools, and stay up to date with the latest trends, tools, and best practices by following industry blogs, subscribing to newsletters, and attending workshops or meetups. By leveraging these resources and strategies, you can get the guidance you need to deepen your knowledge in a specific data engineering domain and advance your career in that area.

I usually ask my questions and doubts on reddit's r/dataengineering, Stackoverflow or LinkedIn groups. When I want to learn a particular skill, I enroll in online course sites like Udemy or Coursera. For guidance on a specific topic, I talk to my mentors, seniors or experts in that domain within my network.

You can engage with a community of professionals who are likely to have experience in the area you're interested in. By asking thoughtful questions and participating in discussions, you can gain insights, learn best practices, and even build connections with others in the field. Additionally, forums often have archives of past discussions that might already address your questions. Overall, leveraging online forums is a great way to tap into collective knowledge and receive practical advice tailored to your needs.

Engage with communities like Stack Overflow, Data Science Central, or specific LinkedIn groups. These platforms allow you to ask questions, exchange ideas, and learn from the experiences of others who have faced similar challenges.

In my case, I always read some topics and training my mind in data using forum as Medium, I configure some alerts to show about interesting architecture or frameworks related to data platform, data governance, data observability, etc. Another interesting thing to do is subcribe in specific forums from data stack could be use slack or maybe another tool, in theses channels we have many people publishing problems about some tool or framework to put in production, it's help a lot when I looking for framework open source on my stack

Leverage LinkedIn, industry-specific networking sites, or professional groups. These networks can connect you with experienced professionals and potentially open doors to collaborative opportunities or professional advice.

I connect with colleagues, mentors, or industry contacts who have expertise in that area. By engaging with my network, I can receive personalized advice, recommendations, and insights based on their experiences. Networking allows me to tap into a wealth of knowledge and perspectives, which helps me navigate challenges and make informed decisions in my data engineering work.

Building a robust professional network is essential for anyone seeking guidance in a specific data engineering domain. By actively engaging with platforms like LinkedIn, attending industry events, joining professional associations, tapping into alumni networks, and participating in online communities, individuals can access a wealth of knowledge, expertise, and mentorship. These networks not only offer valuable insights and advice but also create opportunities for collaboration, career advancement, and staying updated on the latest trends and best practices within the ever-evolving field of data engineering.

I always trying to establish a relation with some people that are interesting in the same field that I work and people that really needs help or some advice to improve their skills at linkedin, another important thing is looking for specific skills that you want to learn, I receive some advice for people that have certificate in aws for example, it help a lot to prepare for the certification, by the way, never desist and try to make contacts for your future that can help you and be humble to retrieve after,

Engaging in online courses tailored to the specific domain of data engineering can provide valuable guidance and skill development. Platforms like Coursera and Udemy offer a plethora of courses covering various aspects of data engineering, including data processing, data warehousing, ETL processes, and more. By enrolling in these courses, individuals can learn from industry experts, gain practical knowledge through hands-on projects, and stay updated on the latest tools and technologies in their chosen domain. Additionally, some courses offer forums or community spaces where learners can interact with peers and instructors to seek guidance, discuss challenges, and share insights, further enhancing the learning experience.

When I need guidance in a specific data engineering domain, I often turn to online courses. These courses are great because they're structured and led by experts, allowing me to dive deep into both foundational and advanced topics. I use platforms like Coursera and Udemy which offers a variety of courses tailored to different aspects of data engineering. What I find most beneficial is that many of these courses include practical projects, which give me a chance to apply the concepts in real-world situations. This hands-on approach not only builds my skills but also boosts my confidence in handling complex data challenges.

I seek out reputable platforms that offer courses related to the topic I'm interested in. By enrolling in these courses, I can learn from industry experts and follow structured learning paths designed to deepen my understanding. Online courses provide me with the flexibility to study at my own pace and access valuable resources such as video lectures, assignments, and forums for discussing concepts with peers. This approach helps me acquire new skills and knowledge relevant to my data engineering pursuits.

Always is positive looking for more knowlegde in data engineering field, there are many bad and good course or training online, regardless of the course, you have to put this into practice maybe creating new projects as your porfolio, I recomend to looking for some platform that is not very knowledge and try to understand if it make sense to you, In my case, I always use youtube or some tech talks about data and a site that really help to improve architectures such us "startdataengineering".

Reading books and academic journals can provide deep insights and a theoretical understanding of data engineering. This is useful for building a solid foundation or exploring advanced topics in depth.

I search for authoritative books written by experts in the field, which often provide comprehensive coverage of relevant topics. Journals also offer valuable insights into the latest research and trends. By reading books and journals, I can deepen my understanding of key concepts, learn about advanced techniques, and stay updated with the latest developments. This helps me gain a solid foundation and access detailed information that complements my practical experience in data engineering.

Look for mentoring programs offered by professional associations, alumni networks, or within your organization. A mentor with expertise in data engineering can provide personalized guidance, career advice, and technical know-how.

Through mentoring programs, I can gain insights into industry best practices, receive advice on navigating challenges, and expand my professional network. Engaging with a mentor allows me to learn from their expertise, ask targeted questions, and receive constructive feedback to enhance my skills and confidence in the data engineering domain.

I actively engage in hands-on projects related to the domain, allowing me to apply theoretical knowledge and gain practical insights. By experimenting with different tools, techniques, and approaches, I can learn through trial and error, identifying what works best in real-world scenarios. This hands-on experience not only reinforces my understanding but also builds confidence and problem-solving skills specific to data engineering challenges. Regular practice and experimentation play a crucial role in my continuous learning and development within the data engineering field.

If your are in a popular data engineering domain such as Azure Data Factory, Microsoft Fabric and Databricks, then you are in luck because there are many books written about them. Thanks to the authors, you can get the whole perspective in that specific domain by reading their books. But if you work in a small domain, such as Matillion and dbt, then it's tougher. You'll need to reach out to the formal user community of that particular tool, rather than a random online forum. Go to Matillion, Informatica or dbt website and find the link to user community under support and resources menu. Post your question and issue there, and people will help you out. Don't forget to pay it forward afterwards though, help other people too.

A significant 77% of survey respondents now consider data engineering to be critical or very important. This increased importance can be attributed to the growing demand for access to analytical data juxtaposed with the rising fragmentation of data sources and targets.

Keep up with industry trends by attending conferences, workshops, and seminars. Engaging with the latest research and innovations in the field can inspire new ideas and improve your skills. Additionally, subscribing to industry newsletters and following key influencers in data engineering can help you stay informed and connected.