Data engineering is the process of designing, transforming, and architecting data infrastructure. Businesses rely on data engineers to create organized, meaningful data to make decisions. Get started in your dream data career with these resources.
Working with data is an exciting career path with in-demand skills. That's why we've collected resources on data engineering and other data topics, including:
AI and machine learning
Data analytics
Data basics
Data science
Generative AI and ChatGPT
Frequently Asked Questions (FAQ)
Data engineering is a subfield of data science responsible for designing, building, and maintaining data infrastructure to collect, process, store, and deliver data so that it can be used and analyzed at scale. Data engineering is extremely important for navigating today’s big data landscape because it enables organizations to generate timely data analysis to guide more effective decision-making.
Data engineers are tasked with the responsibility of preparing massive amounts of data for analysis by data scientists. By using frameworks like Apache Spark to pull data from Hadoop data lakes, data engineers can deliver data for analysis quickly. With the use of machine learning platforms such as TensorFlow, they can train and use neural networks to help decipher unstructured data like video, audio, and image files. And, by using cloud database platforms like Cloudera, data engineers can leverage the power and scalability of cloud-based approaches for their work.
When starting to learn data engineering, you might need to already have strong experience in working with data projects. A four-year college degree in computer science would be highly beneficial, but more often than not, companies might be more interested in someone who has a strong understanding of the fundamentals of computers, software, coding, and programming languages. You will need to have a comprehension of the data engineering ecosystem, databases, and languages like Python, Sequel, and C. It would also help to possess a keen analytical ability to see through the data weeds to offer some insights and understanding to others in your organization.
Yes! Coursera offers a wide range of online courses and Specializations in data engineering and related topics like machine learning and data science. You’ll be taking these courses from top-ranked institutions and organizations like the University of California San Diego, the University of Colorado, Google Cloud, and IBM, so you don’t have to sacrifice the quality of your education to learn online. Coursera also offers the opportunity to get professional certificates in data engineering and data science from Google Cloud and IBM, so you can continue to add to your credentials on your own flexible schedule.
If you are a data engineer looking to level-up or a beginner looking to get an introduction to the field, there are a few great resources available. The Introduction To Data Engineering course covers data engineering fundamentals for all stages of the pipeline. The Python and Pandas for Data Engineering course from Duke University provides an overview of Python libraries and data structures used for building and structuring data engineering applications. Alternatively, the Data Engineering and Machine Learning Using Spark course provides an in-depth look at applying Spark for data engineering and machine learning projects. The Python for Applied Data Science and AI course also covers similar topics related to data engineering but also goes into applications in AI and machine learning tasks. Finally, the IoT, Wireless and Cloud Computing course provides a good entry point to data engineering if you're looking to get familiar with those topics.
![[Featured image] Students learn big data concepts in a classroom.](https://images.ctfassets.net/wp1lcwdav1p1/xGsmrYG2JgCKWvZVxRaXM/e07d67ecf6c676663222b24769fe640e/iStock-543443692.jpg?w=330&h=216&q=60&fit=fill&f=faces&fm=jpg&fl=progressive)
