Hier erfahren Sie, wie Sie während eines Data Engineering-Praktikums aus Fehlern lernen und Feedback einholen können.

In addition to this, you can write automated tests like Assert statements that take in a SQL query and check to see if there is the proper number of entities returned. This makes it so every time you push new code, it runs these statements and either pushes and completes code with no errors, or returns the error and allows you to fix the issue before completing the push.

When you encounter errors, see them as learning opportunities. Analyze what went wrong and use SQL queries to identify and understand discrepancies in your data.

Always go to experienced professionals when you are even the slightest bit confused. They all were in your situation at one point in their career, so they are often more than happy to answer any and all of your questions and give you advice on how to improve for next time. Remember to take notes because the information given will most likely be important to you throughout your career.

Receber feedback de forma construtiva sempre é algo positivo, por mais que o feedback seja negativo ou não, ter uma visão/perspectiva de alguém, olhando de fora seu trabalho é essencial para que voce entenda o que esta errado e no que esta acertando, peça feedbacks para seus lideres e/ou colegas que possuem contanto com seu trabalho diariamente, e não espere o projeto acabar para pedir, peça e aplique para que melhor ainda mais sua entrega como engenheiro

Proactively ask for feedback on your work. Approach supervisors with specific questions to gain valuable insights that can refine your projects and enhance your skills.

Objectively assess feedback and look for actionable advice. Research suggested techniques and apply them to your projects to turn feedback into practical skills.

Data engineering is like a "While Loop" which needs to run until we achieve the optimal solution. It is not just about producing the end data but also includes the efficient data monitoring strategies which help us to fix bugs and errors. In fact, this helps developers to revise their pipelines quickly, and mold them accordingly. Data Engineers serve many stakeholders including Data Analysts and Data Scientists, and each comes with different data requirements. It becomes very obvious to iterate the data solutions according to the requirements.

Implement changes swiftly when you find bugs or receive feedback. Quickly revising scripts or adjusting ETL processes helps you learn in real-time and demonstrate adaptability.

As a data engineer, documenting every detail is crucial and often underrated in measuring progress. Make it a habit to write down every new learning, mistake, lesson learned, and how you overcame it. By consistently doing this, you'll develop an invaluable personal learning kit to help you prepare for future challenges.

Keep detailed records of challenges and solutions. Documenting your learning process creates a personal knowledge base for future reference and consolidates your knowledge.

Discuss your learnings and mistakes with peers to foster a collaborative environment. Sharing optimization techniques or other findings helps reinforce your understanding and benefits the team.

During a data engineering internship, learning from mistakes and seeking feedback is crucial. Actively reflect on errors by analyzing what went wrong and how to improve. Document these lessons to avoid repeating them. Seek constructive feedback from mentors and peers regularly, asking specific questions about your performance and areas for improvement. Embrace a growth mindset, viewing mistakes as learning opportunities. Participate in code reviews and team discussions to gain different perspectives. Continuously apply feedback to your work, showing progress and a commitment to personal and professional development.