One of the best courses in the platform, it will guide you to the fundamental principles of data engineering and you'll build your first pipeline for stream and batch data

A good introductory course that provides a good framework on how to think about the data engineering discipline. I particularly liked the simulated conversations with other stakeholders. They were quite representative of real world interactions. In my opinion, the only downside were the labs. They were too easy and almost entirely involved following direct instructions provided as part of the comments and did not involve any thinking to be done on the learner's part

This course fulfills what it says it will give - make you think like a data engineer. Has exceeded my expectations. And so far, the BEST data engineering course out there.

Excellent course. Good combination of theory with the work with current tools in the market. I would have liked to have more time for reviewing the details and links.

Labs were very much handheld with every step being explained, not much do it yourself

The "Introduction to Data Engineering" course on Coursera provides an excellent foundation for those new to the field. It strikes a good balance between theory and practical application, covering key concepts like data pipelines, cloud services, and how to translate business requirements into technical solutions. The course offers hands-on experience with AWS tools such as Lambda, Glue, Kinesis, RDS, and Firehose, giving learners practical skills to build and manage data pipelines. The mental framework for problem-solving and understanding functional and non-functional requirements is a highlight. Overall, this course is a great starting point for aspiring data engineers, offering both foundational knowledge and hands-on experience with industry-standard tools.

I recently completed the "Introduction to Data Engineering" course by DeepLearning.AI and AWS. The course provided valuable insights and practical knowledge that deepened my understanding of data engineering concepts. The instructor's expertise was evident and truly inspiring. However, I encountered challenges related to IAM permissions while configuring a Lambda function, which hindered my progress. Guidance on resolving such issues would enhance the learning experience for future participants. Overall, I highly recommend this course for anyone looking to delve into the field of data engineering!

The course material is of outstanding quality, featuring challenging labs that effectively stimulate learning. The insightful interviews, especially those involving Sol Rashidi, were a highlight. Joseph Reis, a new instructor to me, impressed with his humility and generosity with the interviewees and content. The updates to the content promise even more value with Morgan Willis, whom I admire, along with the entire AWS e-learning team. I follow them whenever possible, even in gamified interactions on Twitch and on AWS events. Grateful for this enriching experience. +5 stars!

This course was fascinating, useful and well execute. The whole concept of the lifecycle and undercurrents along with reading the book was something I was looking for. In particular, the requirements gathering part was something which I didn't find in other courses/content that I've seen on that engineering, and I was a bit lost before. I feel like I gain important knowledge and also had to think through about the functional and non-functional requirements and tradeoffs of the different services. I look forward to the next courses

I had recently been promoted to a more senior role involving managing projects and working with a broad spectrum of non-technical stakeholders situated relatively high in the business value chain. This course helped me develop a basic mental framework to get by and wrap my head around the ABCs of managing data products. I was surprised to see the fruits of learning so soon. I wholeheartedly recommend this course to technical experts who are also taking their first steps in product ownership and management.

Great course, nice get-your-hands-dirty-quick approach, with lots of theory though 😅 Worth it in the end, even if it feels overwhelming (and maybe a bit boring) at the beginning! P.S., @Joe, you're an awesome instructor, but please try to be more engaging, I found myself losing focus a lot in the videos with only theory -- the other videos about AWS labs with Morgan, simulted interviews, etc., were more lively and focus-grabbing. Could be just me, thanks for all of this, nonethless! 💖

Coming from a background in data analysis and data science, I had always focused on the latter stages of the data lifecycle. This course illuminated the extensive groundwork required to make data analysis possible. It bridged a significant knowledge gap, showing how raw data is transformed into a usable resource for analytics and machine learning.

I thoroughly enjoyed this introductory course! The theory was engaging and informative, but what stood out the most were the hands-on practice labs in AWS, showcasing tools widely used in real-world scenarios. I highly recommend this course to beginners looking to build a strong foundation and reinforce it with practical, real-world use cases.

The DE specialization so far is the best. The instructor, learning materials, and labs are top notch. The only concern I have is regarding the labs. So, if you mess up something and then you want to re-run all terraform it won't. Because there are leftover resources in AWS and you need to go there and remove almost all of them by hand.

This was a really great introduction to the data engineering world, in process like gathering inforrmation, Resi tried to simulate as possible the real process to handle differents stakeholders. The content was really good how ever I would like to go deep in the hands-on laboratory, it was like just to run the terraform scripts.

Although the theory might be boring for some folks, but I found theory very critical to setting up a good foundation to implement efficient data analytics pipeline. It's very important to know to interact with the different stakeholders to gather their data need and bring that to life through the data pipeline

Lots of theory and cursory application of that theory. BUT, I feel like I understand the fundamentals of data pipelines now at a high level. AND, I feel like I understand AWS so much better now. It's like the "closest to the metal" that you can get as a modern software/data engineer.

Excellent course, not too technical the first part, but worth it every second. It helps you build a mental framework to apply in different situations and think like a Data Engineer. Also good labs to put hands-on in some real-world problems.

This first course is what I was looking for. A set of solid bases and guidelines to design professional data systems. The content of this course is really clear and accurate, approaching key topics when you start a new system.

This a great hands-on course with a structured approach covering the full data life cycle from stakeholders' needs to implementations and much more. I recommend it to anyone who develops solutions that depend on quality data

This is truly a comprehensive course . Joe has covered the multiple facets of data engineering in a typical enterprise set up and has been very lucid in the explanations. The method of delivery of the course is very engaging

This introductory course is really good! It gives you the conceptual foundations with which you can start building systems to manage data. It also offers a gentle approach to the use of tools in AWS and their justification.

Really informative and the graded hands-on assignments were really good as well. As an aspiring data scientist / AI engineer, I learnt a lot about data engineering and how it relates to a data scientist. Highly recommended!

Very thorough and applicable to the real world. Joe and his guests provided great insights into who Data Engineers are, their role in the overall business context, and how Data Engineers provide value.

This is a lot of contents for just the first week, but everything is well explained. We have a now a good understanding of some key concepts pertaining to Data Engineering.