It's very hard to follow a bot's voice. Not at all interactive. A machine keeps speaking at a speed. That's not a way to learn something.

To me, this seems like general BS full of buzzwords, but you won't learn anything practical anout how to use these tools

Personally, I've found that the knowledge delivering method should be redone in a more attractive manner. Concretly, the fact of giving too many texts and theories in the slides easily make students get bored and lack of motivation to continue listening. For such kind hands-on courses, we need more and more practical exercises instead of too many theories lectures without showing any demo neither coding in the slides

IBM stopped to maintain/support it's platform where the jupyter exercises are hosted since more than 1 year !

It's a true shame, we discover at the end of the formation than it is impossible to finish it...

It is Presentation deck with AI bot giving Voiceover narration. No one really teaching anything .

Too dry and technical plus I couldn't get some of the codes with the notebook to work. There is a better video especially for Hadoop on Udacity that gives a much better explanation and I do hope you try to make the material more digestable. Each video is trying so hard to cram as much information into it as possible without giving the students much time to understand what is going.

It is a great course on the theory part but there is no practical part " I guess they was depending on the next course in the specialization", also it would be better for more non robotic way in delivering the lectures.

To much concepts and theory, i need more practice examples and less theory concepts

As with other courses created by IBM, this one is just a hard-to-follow, AI-narrated, too-wide-and-yet-too-shallow information dump using a set of slides. You will NOT learn anything with sufficient depth to consider yourself "ready" to start working with Spark in a real-world project. My biggest itch was the quizzes, though. Not only are some of the questions badly/confusingly phrased or contain typos, but also, and perhaps most annoyingly, the answers are heavily opinion based, or flat out wrong. Some of the questions are also entirely irrelevant and out of place when it comes to practical Data Engineering.

The worst course of this specialization by far. The lectures demanded a strong dev background, threw tech concepts in our face without caring no dev people would understand. The project was focused on data manipulation, joins/selects, without exploring the true potentials of Spark. Suggestions: applied study cases, real distributed dataset so we could test the different resources allocations, design and run a real spark environment with real data, not only csvs that fit in the computer memory.

There are three major concerns I have with this course: 1) Content Depth and Structure: The course content feels overly basic, even for an introductory level. The lab exercises are too simplistic and fail to provide meaningful hands-on experience. There is no technical final assessment; the concluding quiz is entirely theoretical. Questions about IBM products or statistics like "the projected growth of data" seem irrelevant and out of place. 2)Lack of Conceptual Clarity and Practical Application: Key concepts like shuffling, grouping, and filtering are not explained or demonstrated in sufficient depth. Including practical examples to showcase their impact would significantly enhance understanding. The course neglects to explain the execution plan in Spark, particularly how it operates and its implications for application performance. This is a critical topic that deserves proper attention. The explanation of differences between RDDs and DataFrames is confusing, even for someone with basic knowledge. Similarly, the coverage of Spark SQL and functions lacks clarity and structure. A more straightforward approach—e.g., showing three ways to accomplish a task, comparing them, and contextualizing their usage in real-world scenarios—would be far more effective. The inclusion of Pandas is unexpected. While it’s noted that Spark RDDs/DataFrames can be created from Pandas DataFrames, there was too much emphasis on it, and in the same stage of course foundational functions like read.csv are not even mentioned. This omission contributes to a sense of disorganization and a lack of a coherent teaching strategy. 3)Presentation and Delivery: The video materials are AI-generated, and this detracts from the learning experience. Personally, I found the videos too short, with unnecessary and repetitive intro/outro segments that quickly became irritating. This format undermines engagement and suggests a lack of thoughtful design. A human instructor narrating the content could provide a more engaging and dynamic learning experience. A human presenter might also recognize and address the lack of substance in the explanations, resulting in clearer and more effective teaching. Overall, the course feels like a collection of loosely connected topics rather than a carefully designed curriculum. A greater focus on depth, practical application, and a more personalized delivery would significantly improve the learning experience.

All the thinks I need to know about Big Data, Spark, Hadoop and Hive and explained in details

Fantastic blend of theory and practical (labs). The labs are short and have concise material.

Since I already had a previous banckground using Spark this course refreshed the theoretical foundations about its main topics: RDD, parellelism amog other. It was good to remember the way Spark works. On the other hand I must say there are some troubles with some labs environments where is not possible to work on the practical exercises.

Good course on the theoretical concepts needed to understand these tools. However, there is not much practice.

No project. Labs are only copy-paste commands.

A lot of contents that are only explained using text (it will be nice if diagrams, images, examples are more used!)

In general, it's a OK course, with the following course it could be a overall good in order to start your Big Data Journey!

i feel, it should be good if they add more hands-on work for learning spark and Hadoop

Muy pocos ejemplos prácticos para comprender el funcionamiento. La teoría se podría aplicar con muchos casos prácticos fácilmente aplicable en los hands-on-lab como en otros cursos que he completado anteriormente.

The course provided a clear and practical introduction to Big Data concepts using Spark and Hadoop, making complex topics easy to understand. Hands-on examples and real-world scenarios helped build strong confidence in working with distributed data systems.

I found this course very interesting, great for an aspiring data engineer! It starts by introducing the concept of big data in general and then goes into more and more detail, analyzing Hadoop and Spark in depth. I highly recommend it!