In this course, you’ll model, transform, and serve data for both analytics and machine learning use cases. You’ll explore various data modeling techniques for batch analytics, including normalization, star schema, data vault, and one big table, and you’ll use dbt to transform a dataset based on a star schema and one big table. You’ll also compare the Inmon vs Kimball data modeling approaches for data warehouses. You’ll model and transform a tabular dataset for machine learning purposes. You’ll also model and transform unstructured image and textual data. You’ll explore distributed processing frameworks such as Hadoop MapReduce and Spark, and perform stream processing. You’ll identify different ways of serving data for analytics and machine learning, including using views and materialized views, and you’ll describe how a semantic layer built on top of your data model can support the business. In the last week of this course, you’ll complete a capstone project where you’ll build an end-to-end data pipeline that encompasses all of the stages of the data engineering lifecycle to serve data that provides business value.
CC
I like this course since it wrap up everything we learn through these months in the final project.
RG
Starts slow at first but things really start to click and you understand the whole picture towards the end.
AR
great course very comprehensive, some tasks are maybe to easy so you dont really learn what you are doing if you dont want to
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The quality of the course is high. But the final capstone project is rather frustrating with constant terminal crashes, disconnections, and all sorts of errors.
This course is terrible. For the 2 assignments there are a lot of file that need to complete the code. But only give 2 hours time for these two assignment. I unable to complete the assignments due to limited time. Th instructor should let the learner to extend the time if needed. This is not a free course. This is pay course in order to get the certification. I don't understand why give tons of file to complete the code but never give learner to extend time for the assignment. I don't mind to do a lot of exercise but the problem that I need is more time.
I was one of the biggest fans of the course specialization. However, this last part is constantly producing bugs. It is making the progress super slow and you need to prolong the subscription in order to do it. It is good that the labs are covering broad specter of the process - but when you have to just copy and replace values why is there a time frame in which you need to finish it. Nog good enough explanations where the changes are required.
I like this course since it wrap up everything we learn through these months in the final project.
Final capstone project combined all learn concepts.
I think this course is trying to cover a lot more than it should. If I am not wrong, that's true for all courses in this specialization. I don't think it's suitable for beginners or even so called intermediate data engineers.
The course overall was excellent, with well-structured and accessible material. However, there were some issues with visualization in the lab exercises using Superset. In the first instance, the endpoint for accessing Superset did not work at all – the page was unavailable. In the second instance, despite following the instructions to log in with admin/admin, the login credentials did not work no matter how many times I tried. These issues made completing the lab exercises a bit challenging. It would be great if these technical problems could be resolved in the future.
I've just finished the whole program on Data Engineering and found it great! Awesome contents, pace, communication, conversations with guests, and just about the right level of "Challenge" for me. I had lots of fun and learned a lot!
Really like it.
unfortunately the labs were not available for almost a week. plus would have preferred to have each lab, especially in the capstone project, have longer availability than 3 hours. Some of us are new to this and it takes time to find and correct mistakes. While learning and practicing the right approach, helps to not run against the clock. I am attempting to run the lab for the 3rd time, only because by the time i fixed my errors, time has ran out...
really shitty assignment experience IMHO. bad UX/UI (no firefox support, bad handling at chrome). assignments are a bit brain dead but setup easily crashes so just a waste of time. session expires and terraform throwing more errors than orange pedophile clown spreading lies.
the course provides a good foundation for a role as data engineer, including a good understanding of cloud provider solutions(regardless which one you're using). The lecturers (Joe and co.) were clear in their explanation and the lab assignment exercises are moderately challenging, its' more often fill in the blanks, but it was also sometimes convenient as it could be quite overwhelming working through many scripts to understand the whole picture. It's a solid basis to get the lingo right as A data engineer.
The courses in Coursera Data Engineering Professional are quite good, informative and practical. A lots of hands on lab could enhance your data engineering project experiences. I'd like to highly encourage you go through each module thoroughly if you want to be a professional data engineer. Additionally, the course series also pave the way to data architect.
great course very comprehensive, some tasks are maybe to easy so you dont really learn what you are doing if you dont want to
Starts slow at first but things really start to click and you understand the whole picture towards the end.
Time well spent. 5/5
Very useful course!
Amazing course!
Greate lectures, brain-dead exercises. Auditing only would be my suggestion