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Avis et commentaires pour d'étudiants pour Building Batch Data Pipelines on Google Cloud par Google Cloud

4.5
étoiles
1,525 évaluations

À propos du cours

Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load 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 will get hands-on experience building data pipeline components on Google Cloud using Qwiklabs....

Meilleurs avis

UB

27 mai 2020

A great course to help understand the various wonderful options Google Cloud has to offer to move on-premise Hadoop workload to Google Cloud Platform to leverage scalability of clusters.

AD

16 juil. 2020

Great course learning what it is the big advantages of using GCP for data given they have big implementations and with better performance of what it is today in on premises scenarios

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151 - 175 sur 192 Avis pour Building Batch Data Pipelines on Google Cloud

par Ishwar C

6 mai 2020

The dataflow part was not well explained, especially the labs.

par Etienne M

3 avr. 2020

The course is very useful, but sometimes the labs were strong.

par RK

2 févr. 2020

Decent intro to data pipelines in GCP

par Akshay T

8 mars 2021

Covered lot of topics and services!

par Youcef B

10 mai 2020

it's important to rerun this cours

par David O

23 févr. 2021

Good overview of the topic.

par Francisco M

12 oct. 2020

Very Good!

par dumebi j

23 nov. 2021

good

par Abhishek D

28 juin 2020

Good

par SAJID M W

14 janv. 2020

good

par Jon C

1 oct. 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.

par Franz H

13 juin 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.

par Diego T B

20 sept. 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.

par Alin P

19 mai 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.

par Justin A B

10 juil. 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.

par Brian S

25 nov. 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

par Benjamin T

8 janv. 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

par Sean W

21 déc. 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..

par Sreenu A

14 juil. 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.

par Aaron H

9 nov. 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

par Kota M

31 janv. 2020

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

par Juan J T

11 juil. 2021

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

par Laurence M S

8 avr. 2020

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

par Mariia Z

26 avr. 2020

Good materials, but poor quality of the labs

par Roberto P

16 avr. 2022

The exercises and quizzes are too simple.