Here's how you can master self-study in data engineering.

I learned SQL Basic in Wecommit.com.vn. After that I the checklist of most common pitfalls in database optimization. Obtaining additional database skill such as Oracle, MySQL, SQL Server... and attend weekly zoom call with friends who also work in Database to learn about real use cases. This is how I master my study in data engineering. Thanks all

In my experience, Data engineering skills revolve around collecting, organizing, reading, querying, and discovering business insights from data. The foundational skill for most of this is SQL. So, to start a data engineering career, you must master SQL from the basics to the advanced level. Then, you can move into learning data collection, followed by distributed processing techniques. The final step of your learning should be reporting and visualization.

I think knowing below 2 skill sets are a good starting point 1. SQL 2. Python/ any other OOP language. Along with the above, you need to master the following concepts 1. ETL 2. Data Warehousing 3. Batch and Streaming processing of data For gaining the above knowledge, free materials are available on the internet e.g. Google/YouTube etc.

Begin by mastering the fundamental concepts of data engineering, including data structures, algorithms, databases, and data processing techniques. Establish a solid understanding of key principles to serve as a strong foundation for your learning journey.

Data engineering involves designing, building, and maintaining systems for collecting, storing, and analyzing data. It focuses on the infrastructure that supports data generation, flow, and storage. Data pipelines, ETL (Extract, Transform, Load) processes, data warehousing, data lakes, big data processing, and real-time data streaming. Gain proficiency in SQL, understand basic data modeling, and learn a programming language like Python. Develop expertise in designing complex data pipelines, master cloud-based data engineering tools, and gain knowledge in distributed systems and big data technologies.

Mastering self-study in data engineering begins with grasping core concepts. Start with SQL for data manipulation and management within Database Management Systems (DBMS). Then, delve into Data Modeling to understand structuring and relationships. Python is essential for scripting and creating robust data pipelines. Familiarize yourself with ETL (Extract, Transform, Load) processes, which are crucial for moving and transforming data. Big data frameworks like Apache Spark and Hadoop are vital for handling large-scale data. Lastly, enhance your skills with cloud platforms such as AWS, Azure, and GCP to manage and deploy data solutions efficiently. These foundational skills will set you on the path to becoming a proficient data engineer.

The practical hands on of the data core concepts is the next gradual step towards applying and improving the learned skills. - Start with getting a smaller datasets like Airport, sales repo data etc , kaggle is your friend here. - Get the basic 3NF or Dimensional model of the data set and try creating a data model out of it. - Practise ingestion of the 'EL' layer - Apply the SQL and Python skills for data manipulations and transformations , the 'T' layer of ELT. - Aim is to make the raw data set picked earlier as much analytics ready as possible Remember do not shy away from making mistakes , aim is to get the hands dirty and actually play the game and not be a mute spectator. The only way one can learn master any subject matter.

Focus on acquiring practical skills through hands-on experience with real-world data engineering projects. Work on building data pipelines, managing databases, and implementing data processing workflows using relevant tools and technologies commonly used in the field.

It is good to know the theory, but you will only properly learn by practise. Try to set up your own data base, built a small project like gathering data from a public API into the data base, doing some analysis on the data. Hands-on experience definitely most crucial - and will impress any job interviewer over pure theory.

Document you learnings online. Create small projects involving the data engineering concepts learnt and share on github. Focus on realworld issues of handling and cleaning bad data, logging mechanisms and alerting on your data pipelines. Start from basic usecases of batch processing to advanced streaming usecases. Practuce, seek feedback and get doubts clarified by the community.

Understanding the end-to-end flow of data and solution architecture has been very helpful in growing your career. When it comes to mastering advanced topics, it is always end-to-end data flow; how you solve the problem by designing and developing the data flow through your solution architecture is the key.

Once you've mastered the basics, explore advanced topics such as distributed computing, cloud technologies, big data frameworks, and machine learning integration. Dive deeper into specialized areas of interest to expand your knowledge and skill set

As you get comfortable with the basics, move on to advanced topics to deepen your knowledge. Explore data lakes, which store large volumes of raw data in its original format. Learn about stream processing with tools like Apache Kafka for real-time data handling. Get acquainted with cloud data services from AWS, Azure, or Google Cloud Platform, which are vital in modern data engineering. These advanced areas will enhance your skills and prepare you for complex challenges.

Advanced Topics: Once you have a good grasp of the basics, delve into advanced topics such as big data technologies (Hadoop, Spark), cloud computing (AWS, Azure, Google Cloud), and real-time data processing (Kafka, Flink). These advanced skills are highly sought after and will significantly enhance your capabilities as a data engineer.

Diving into advanced topics keeps your skills sharp and relevant. I made it a point to explore machine learning, big data analytics, and cloud computing. Enrolling in online courses and attending webinars on these subjects helped me stay updated with the latest advancements. For instance, learning about distributed computing and parallel processing enabled me to optimize data workflows at Qodek, making our processes more efficient and scalable.

Use the following for your advantage. - Github to share your projects - LinkedIn to write and share about your findings, learnings - Substack/Hashnode to document your learnings - Stackoverflow to post relevant doubts and questions - Hackathons to learn to solve new problems, collaborate and learn from other developers. - Follow Data engineering forums, groups, key contributors to learn about the field

Engage with the data engineering community through online forums, discussion groups, and social media platforms. Participate in discussions, ask questions, and share your insights to learn from others and stay updated on the latest trends, best practices, and tools in the field.

Engage with the data engineering community to enhance your learning. Join forums, attend webinars, and participate in online discussions to stay updated on industry trends and learn from experienced professionals. Networking can also lead to mentorship and collaborative projects, offering valuable insights beyond self-study.

Community Engagement: Engage with the data engineering community. Join forums, attend webinars, and participate in online courses and workshops. Platforms like Stack Overflow, Reddit, and LinkedIn groups are excellent for networking, finding mentors, and staying updated with the latest trends and best practices.

Engaging with the data engineering community accelerates learning. I joined forums like Stack Overflow, participated in LinkedIn groups, and attended local meetups. By discussing challenges, sharing solutions, and collaborating on projects with peers, I gained diverse perspectives and practical insights. These interactions also helped me build a professional network, which proved valuable for career growth and staying informed about industry trends.

Continuous learning makes you resilient to inevitable change that will come in future. The amount of new technologies that appear in data engineering every year is mind boggling. It is impossible to follow new developments and hard not feel lost or frustrated. But you have to go with the flow at your own pace and decide where you want to allocate your weekly learning hours. Don’t be rigid and stick to what you know. I am quite impressed by an old interview of Bruce Lee where he talks about being like water. You have to be flexible and adaptable like water to get ahead in life. Water takes the form of whatever container it is in. So always learn and adapt, be like water my friend.

Embrace a mindset of continuous learning and professional development. Stay curious and actively seek out new learning opportunities, whether through online courses, tutorials, workshops, or conferences. Dedicate time each week to expand your knowledge and skills in data engineering.

Data engineering is constantly evolving, so continuous learning is essential. Subscribe to blogs, follow thought leaders on social media, and seek out new courses or certifications. Staying updated ensures you remain competitive and knowledgeable about the latest technologies and best practices.

Engage in a Community Setting: Participate in online forums, attend meetups, and collaborate on projects with other learners to network, learn from others, stay accountable, and stay motivated. Take Breaks and Reflect: Give yourself time to reflect on what you've learned to connect the dots and gain new insights.

Continuous Learning: Data engineering is a rapidly evolving field. Commit to lifelong learning by regularly updating your knowledge base. Follow industry blogs, subscribe to newsletters, and take advantage of MOOCs (Massive Open Online Courses) from platforms like Coursera, edX, and Udacity. Continuous learning ensures you stay current with new technologies and methodologies.

? It's a CRAP !! C - Concept clearance is must to get the right fundamental understanding. R - Read official documentation to get to know the right implementation & best practices. A - Approach the root problem systematic way and then try to find multiple solution patterns / libraries available. P - Projects help you get all the obstacles implementing a real world solution, which will act as catalyst for your growth.

Apply your knowledge and skills to real-world projects to reinforce your learning and gain practical experience. Take on personal or freelance projects, contribute to open-source initiatives, or collaborate with peers on data engineering challenges to put your skills into practice.

Project Application: Apply what you learn by working on personal or open-source projects. Building end-to-end data pipelines, creating data warehouses, or developing data-driven applications will provide practical experience and showcase your skills. Document your projects on GitHub to create a portfolio that can impress potential employers.

To showcase your project work effectively, document your project's objectives, the technologies used, challenges faced, and the outcomes achieved. This documentation will not only serve as a personal record of your learning journey but can also be an impressive part of your professional portfolio. Consider participating in hackathons and data challenges that can provide a competitive and collaborative environment to apply your skills. These experiences are highly valued in the industry and can provide a significant boost to your career in data engineering.

Na minha experiência o prático é o mais efetivo. No mercado observamos profissionais que ensinam e explicam conceitos que n?o conseguem aplicar no mundo real e isso é ruim, seja efetivo no que você faz. Se a empresa precisa de uma ETL, fa?a a ETL primeiro, se o prazo permitir use as boas práticas pois elas ser?o usadas no futuro, porém nem sempre isso será possível. Infelizmente ter?o projetos sem documenta??o efetiva, ter?o códigos que n?o poder?o estar organizados o suficiente e isso n?o pode acontecer o tempo todo, porém acontecerá. Caso tenha um time robusto e grande com pessoas desenvolvendo diversos papeis, dificilmente passará essas dificuldades, mas no mundo real nem sempre é possível ter todo esse recurso, seja prático primeiro.

From conversations with many students: To master self-study, you should also consider ... Structured learning path: Focus on a structured learning path that includes core concepts, practical skills and advanced topics. Start with foundational knowledge and gradually work your way up to more complex topics. Practical application: Apply what you've learned in projects and real-world scenarios. Practical experience consolidates theoretical knowledge and improves problem-solving skills. Business relevance: Align your learning with business needs and industry trends. Understanding how data engineering impacts business decisions will help you stay relevant and demonstrate your value in your role.

Consider creating a structured study plan with clear learning objectives, milestones, and timelines to track your progress and stay motivated. Additionally, leverage online resources such as textbooks, documentation, and tutorials to supplement your self-study efforts and deepen your understanding of data engineering concepts and techniques. Finally, seek feedback from peers or mentors to help identify areas for improvement and guide your learning journey.

Here’s what else to consider: Besides technical proficiency, focus on developing soft skills such as problem-solving, critical thinking, and effective communication. These skills are vital for collaborating with teams, understanding business requirements, and delivering impactful data solutions. Additionally, consider obtaining relevant certifications to validate your expertise and enhance your resume.

Beyond technical skills, developing soft skills is equally important. Effective communication, teamwork, and project management skills are critical for a successful career in data engineering. I invested time in improving these areas through workshops and practical experience. Balancing technical prowess with strong interpersonal skills has been key to my success as a CTO, enabling me to lead teams effectively and drive strategic initiatives at Qodek.

CI/CD is very important nowadays. Learn tools like jenkins and GitHub. When you are confident in cloud computing go for a certification, usually they expire in a span of 1 year. When it is about to expire it sends a reminder for renewal and in the updated course you can find the change log and learn new tools or settings that have been updated.