You're starting a data engineering internship. What are the most important things to learn?
Data engineering is a fast-growing and exciting field that involves building, managing, and optimizing data pipelines and systems. If you're starting a data engineering internship, you might be wondering what are the most important things to learn and how to make the most of your experience. In this article, we'll share some tips and advice on how to prepare for your data engineering internship and what skills and tools you should focus on.
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Adedotun Adeboye, MSc.Data Specialist || Data Engineer || Tutor || TopWomenTech 200 - Expert in Data Lifecycle Management from Extraction to…
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Akshat DashoreAssistant Manager - Cloud Architect | Analytics | Big Data Engineer at Metlife | Microsoft Certified | Generative AI…
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Carlos Fernando ChicataSome community Top Voice badges | Data Engineer | AWS User Group Perú - Arequipa | AWS x3 |
Before you dive into your internship project, you should have a solid understanding of the core concepts and principles of data engineering. This includes data modeling, data quality, data integration, data warehousing, and data governance. You should also be familiar with the data engineering lifecycle, which covers how to plan, design, build, test, deploy, monitor, and maintain data pipelines and systems. You can learn these basics from online courses, books, blogs, podcasts, or mentors.
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Adedotun Adeboye, MSc.
Data Specialist || Data Engineer || Tutor || TopWomenTech 200 - Expert in Data Lifecycle Management from Extraction to Consumption
Just with programming languages, you must adopt the 'learn by doing' approach. Also, the best data personnel is not necessarily one with all the answers, but one who has access to resources when needed!..... So, in addition to your learning, make sure you build a knowledge base for yourself that you can access when needed.
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Akshat Dashore
Assistant Manager - Cloud Architect | Analytics | Big Data Engineer at Metlife | Microsoft Certified | Generative AI Learner
A typical Data Engineering lifecycle includes architecting data platforms and designing data stores. It also includes the process of gathering, importing, wrangling, cleaning, querying, and analyzing data. Systems and workflows need to be monitored and finetuned for performance at optimal levels.
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Sachin D N 🇮🇳
Data Consultant @ Lumen Technologies | Data Engineer | Big Data Engineer | Azure | Apache Spark | Databricks | Delta Lake | Agile | PySpark | Hadoop | Python | SQL | Hive | Data Lake | Data Warehousing | ADF
Focus on understanding and gaining hands-on experience with various data systems and tools. Learn how to design, build, and maintain data architectures, including databases and large-scale processing systems. Familiarize yourself with languages like SQL for database management, and Python or Java for scripting and data pipeline development. Grasp the concepts of data modeling and how to transform raw data into a more usable format through ETL (Extract, Transform, Load) processes. Understand the basics of distributed systems and cloud platforms like AWS, Google Cloud, or Azure, as they are often used for big data processing. Lastly, learn about data warehousing solutions and big data technologies like Hadoop and Spark.
Data engineering requires a variety of tools and technologies to handle different aspects of data processing and analysis. Depending on your internship role and project, you might need to use programming languages such as Python, SQL, Java, or Scala; data frameworks like Spark, Hadoop, Kafka, or Airflow; data platforms like AWS, GCP, or Azure; data storage such as MySQL, PostgreSQL, MongoDB, or Cassandra; and data visualization tools like Tableau, Power BI, or Matplotlib. While you don't need to be an expert in all of these tools, you should have a working knowledge of the ones that are relevant to your project and be able to learn new ones quickly. Practicing with these tools can be done by following tutorials, reading documentation, or doing mini-projects.
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Carlos Fernando Chicata
Some community Top Voice badges | Data Engineer | AWS User Group Perú - Arequipa | AWS x3 |
Learn about that features and theory behind it that tool gives you: the tool comes and go, but the knowledge of feature it's more important. Too learn how tool will help to solve problem in what part of the data engineering space & how it relates with another parts: for example, airflow can use to orquestation but it can work with metadata management too.
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Data engineering not only requires technical skills, but also best practices and standards that guarantee the quality, reliability, and efficiency of data pipelines and systems. It is important to document code, data models, and data flows. Additionally, writing clean, modular, and reusable code, following coding style and naming conventions, using version control and testing tools, automating data pipeline tasks and workflows, monitoring and troubleshooting performance and issues, optimizing resources and costs, implementing security and privacy measures, as well as communicating with your team and stakeholders are all essential practices for delivering high-quality data products. All of these best practices will help ensure that the business needs and expectations are met.
Data engineering is not without its difficulties, and during your internship you may come across various issues such as data complexity, quality, scalability, security, and ethics. For example, data can come in various formats and sources, making it complex to process and analyze. It can also be incomplete or inconsistent, which could affect the validity of the data analysis. Additionally, data can grow rapidly and unexpectedly, potentially straining the data pipeline capacity. Moreover, it could be sensitive or confidential, posing risks of data breaches or misuse. Lastly, data can be biased or misleading, raising ethical and social issues. It is important to be aware of these challenges and have strategies in place to address them.
Data engineering is an ever-evolving and expanding field that requires constant learning and growth. After your internship, use it as a stepping stone to further your data engineering career. To keep learning and developing, seek feedback and mentorship from your supervisor, colleagues, or peers. Reflect on your internship project and outcomes, explore new data engineering tools and techniques, follow and join data engineering communities and networks, read news, blogs, podcasts, and videos related to data engineering, and enroll in data engineering courses or programs. Through staying curious and motivated, you can improve your data engineering skills and knowledge to advance your data engineering career.
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Adedotun Adeboye, MSc.
Data Specialist || Data Engineer || Tutor || TopWomenTech 200 - Expert in Data Lifecycle Management from Extraction to Consumption
Attend data conferences. Data Trends and Future plans are usually revealed in these conferences. This will help you understand the industry better and stay ahead of anticipated changes.
Data engineering is a rewarding and fulfilling field that offers many advantages for your personal and professional development. During your data engineering internship, you can apply your skills and knowledge to real-world problems, gain valuable experience and exposure to projects and environments, learn from experts, build your portfolio, expand your network and connections, and discover your interests and passions. By taking advantage of these benefits, you can make the most of your data engineering internship and prepare yourself for future endeavors.
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