学生对 Google 云端平台 提供的 Building Batch Data Pipelines on Google Cloud 的评价和反馈
课程概述
热门审阅
UB
May 27, 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
Jul 16, 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
101 - Building Batch Data Pipelines on Google Cloud 的 125 个评论（共 192 个）
创建者 김태규
•Sep 13, 2020
NICE LECTURE
创建者 Arghya N
•Jun 21, 2020
great course
创建者 Milos B
•May 31, 2020
Great course
创建者 Vishal M
•Aug 21, 2021
Good Course
创建者 Naveen K B V
•Jun 13, 2020
Excellent!.
创建者 Dheeraj A
•May 11, 2020
A Good One!
创建者 Ryan B
•Apr 24, 2021
Thank you!
创建者 Diego S
•Jun 12, 2020
Excellent!
创建者 Fatima-zahra K
•Mar 22, 2020
magnifique
创建者 Mirelys D
•Jun 25, 2020
Excelente
创建者 Neftali M
•May 25, 2020
EXCELLENT
创建者 Ömer F B
•Jan 24, 2020
Great Job
创建者 Trusha R S
•May 5, 2020
good one
创建者 Muhammad Z H
•Jun 5, 2020
Thanks
创建者 Ganesh K
•May 4, 2020
great
创建者 Felipe A
•Jul 19, 2021
good
创建者 Shweta M (
•Dec 26, 2020
BEST
创建者 Gerardo F V
•Nov 23, 2020
good
创建者 Priyanka C
•Jun 14, 2020
Good
创建者 Cristhian R B
•May 17, 2020
Thx!
创建者 CH V S
•May 6, 2020
Nice
创建者 Santanu R
•Apr 27, 2020
Good
创建者 Koushik C
•Aug 3, 2020
OK
创建者 Kowsik P
•Dec 20, 2020
The course covers the main stuff required to understand the lifecycle of batch processing along with hands-on labs. However, when we have complimentary services offered by GCP which is Data Flow on top of the Data fusion which serves a similar purpose so it's much necessary to explain the use cases holistically when to go for Data flow and Data fusion. Also this 3rd of 6 courses as part of DE certification, it has more code content explained.
创建者 Jeremy G
•Dec 21, 2021
I thought this course was good but would have been better if it went a bit deeper into the infrastructure that the different Pipelines tools run on in GCP, particularly for Dataflow. I understand that Dataflow is serverless but a better explanation of what that means as jobs are running would have been helpful.
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