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Learner Reviews & Feedback for Building Batch Data Pipelines on Google Cloud by Google Cloud

1,678 ratings

About the Course

Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs....

Top reviews


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Excellent course with appropriate explanation on cloud data fusion, data composer, data proc and cloud data-flow. Must learn course for all aspiring Big Data Engineers.


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Good contents. Some lab questions do not have answers. Hope provide them to know how I understand the knowledge.

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176 - 200 of 208 Reviews for Building Batch Data Pipelines on Google Cloud


Jan 14, 2020


By Jon C

Oct 1, 2020

Enjoyed the course and the instructors. There is a lot of ground to cover for two weeks worth of content. Some minor improvements: 1. A number of the videos mention linking to content (template github as an example), but then failed to include a link in the resources section. 2. The labs are more of a code review than practice in creating actual pipelines, and ask questions without providing an answer. It may prove helpful for learners to have an opportunity to develop elements of the lab code as well as having answers to the review questions so that the lab user knows whether or not their answer to the questions posed were in fact correct.

By Franz H

Jun 13, 2020

Again one of the mostly presentation classes - a filmed version of a feature desription of Google products. Some useful demos included, but both the quizzes and the labs are without even the most elementary demands - so it is really hard to learn anything. Very easy to collect another certificate, but that's about it. It shows that you successfully walked around the car and can name some of its parts, but you will not learn to drive in this class, unless you use the generously provided labtime for studies of your own.

By Diego T B

Sep 20, 2020

This course only scrathes the surface of Batch products of GCP. On the Dataproc lab, which in my opinion is the most important for data engineers working with GCP, you have very little time to do so much work, that you have to speed run it and learn nothing at all. The Week 2 course could be split up into another week.

By Alin P

May 19, 2020

The lab assignments could be more involved than copy pasting some commands, which is useful, but easy to forget. The videos are quite long. There should be more quizzes that tested the knowledge in the videos more thoroughly, i.e. keep the rapid feedback of the quizzes, but rotate the answers.

By Justin A B

Jul 10, 2020

Would like the labs to center around building common ETL requirements in the Dataflow portions of the labs, example joining, data transforms, pivots, etc. Most ETL developers are familiar with these patterns and would be interested in mapping those with how Dataflow would solve for.

By Brian S

Nov 25, 2020

Many of the labs didn't really provide opportunities for real hands on learning, but instead seemed to be button clicking experiences. Improvements could be made by not just having students run the files, but also make updates to them as well

By Benjamin T

Jan 8, 2021

Course needs many improvement: Include better explanations, walk throughs through the very particular apache beam syntax and logic as well as give hints and time in qwiklabs for experimentation particularly for Data Flow

By Sean W

Dec 21, 2020

the first part was great, however there were many times when cloud data flow was covered.. streaming topics were discussed. Why in this course? I know that cloud data flow can do both, but don't mix the material..

By Franz R

Oct 5, 2022

Most of the labs need to be review to make sure the instructions are still correct. I spent a substanial part of my time in the labs finding work arounds because of poor instructions.

By Sreenu A

Jul 14, 2021

It covered mostly a basic stuff. Data Engineers need in depth knowledge. Qwiklabs need to modify as real time scenarios instead of working on gcloud commands.

By Aaron H

Nov 9, 2021

this course is OK, the information is good but the labs are messed up 90% of the time, and like always to much sales pitch

By Kota M

Jan 31, 2020

It is helpful as a first step, but it does not make learners who can develop architecture on the google cloud.

By Juan J T M

Jul 11, 2021

There is very good material, but it should be a thorough examination of the different tools and its code

By Laurence M S

Apr 8, 2020

This course was extremely confusing. I will most likely need to go through it again.

By Mariia Z

Apr 26, 2020

Good materials, but poor quality of the labs

By Roberto P

Apr 16, 2022

The exercises and quizzes are too simple.

By Marco A d A C

Mar 2, 2021

I expected more details, more deepness

By Y C

Aug 19, 2020

Could elaborate more on dataflow

By Hossain A

Aug 25, 2020

Got an overview of GCP pipeline

By Raj C

Jul 25, 2022


By Yogesh D

May 28, 2020

The course at a very high level, students with no prior exposure to HDFS, SPARK and Apache beam will have hard time understanding any concepts. Labs are not productive enough, you just follow instructions, labs should be more challenging

By Lourdes R

May 25, 2020

I think the examples in the lab could be more interesting with examples using data set closer to business reality.

Also, some tutorials contain wrong steps and references to old tools

By Lisanul D

Mar 3, 2021

DataFlow part is really bad, no explanation in the lab excercises. Anyone could run them blindly and go through them. No way to verify if the lab understanding was good.

By Marcos “ P

Sep 16, 2021

Some slides are missing in the resources, doing difficult to follow the video and take note. I would prefer to have material to read instead to follow videos