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Building Resilient Streaming Systems on Google Cloud Platform(으)로 돌아가기

Google 클라우드의 Building Resilient Streaming Systems on Google Cloud Platform 학습자 리뷰 및 피드백

4.6
stars
2,011개의 평가
159개의 리뷰

강좌 소개

This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn how to build streaming data pipelines using Google Cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audience. Prerequisites: • Google Cloud Platform Big Data and Machine Learning Fundamentals (or equivalent experience) • Some knowledge of Java Objectives: • Understand use-cases for real-time streaming analytics • Use Google Cloud PubSub asynchronous messaging service to manage data events • Write streaming pipelines and run transformations where necessary • Get familiar with both sides of a streaming pipeline: production and consumption • Interoperate Dataflow, BigQuery and Cloud Pub/Sub for real-time streaming and analysis...

최상위 리뷰

PG

Aug 25, 2018

This course was very helpful to understand how to built high throughput streaming work flows on google cloud. It described in detail how to model big table for efficient application.

CC

Aug 19, 2017

Course gives nice overview of Bigtable, when to use it compared to bigquery. flowchart describing the when to use which product is really helpful. Thanks Lak for the course.

필터링 기준:

Building Resilient Streaming Systems on Google Cloud Platform의 158개 리뷰 중 126~150

교육 기관: Serge B

Oct 31, 2017

Good course and labs. I wish the code sample didn't keep switching between Python and Java, it can be confusing.

교육 기관: José G M

Jul 08, 2019

Creo que hacen falta ejercicios de programación en la plataforma de google desarrollados por el alumno.

교육 기관: Nick D

Aug 11, 2017

Good content, narrative, videos, and labs. Quiz questions are perhaps a little easier than expected.

교육 기관: Karn D

Nov 04, 2019

Good course overall. Would've liked more "do it yourself" activities, but otherwise - it was fine.

교육 기관: Mouafek A

Jan 04, 2020

there was a problem in the SSH console in one of the labs, but otherwise, it's a great course!

교육 기관: Aadil S

Jun 15, 2019

Good Course to get a starting point on dealing with streaming data on GCP

교육 기관: RAJKUMAR

Oct 16, 2019

Some of the samples are in Java which makes the lab not of much use.

교육 기관: Kishore K P

Mar 22, 2019

exposure on bigtable and streaming process

교육 기관: Dmytro C

Aug 27, 2019

More quizzes would be nice

교육 기관: Paul H

Mar 27, 2018

Good, but fairly basic.

교육 기관: Rasmus B

Dec 18, 2018

Very helpful overall

교육 기관: Prashant G

Oct 31, 2019

good content

교육 기관: HARIKRISHNA G

Aug 05, 2018

Useful

교육 기관: Subhrendu B

Jul 26, 2019

good

교육 기관: Ashwin S

May 28, 2019

good

교육 기관: Alvaro R F

May 05, 2019

Cool

교육 기관: Alberto C V

Oct 26, 2018

N/A

교육 기관: Narendra B O

Aug 25, 2019

.

교육 기관: Miguel R

Jan 03, 2018

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교육 기관: lee.simon3

Jan 16, 2020

The way things are introduced lacks the rigor of reasoning and has a lot of implicit context changing. For example, scalability is mainly not related to hardware failures, but rather related to changing volumes of traffic. The introduction was

"So when we talk about scaling here, we're essentially talking about being able to deal with faults, as clients and servers and storage systems ,etc., fail unexpectedly. "

It may be relevant to the benefit of using PubSub, but it's not relevant to the changing volume of streaming solutions which was the context of discussion at that point. It seems the speaker was talking about fault tolerance. Why use the term "scaling"?

There are other examples like this. The speaker demonstrated some communication tricks, with techniques to help remembering things. But more solid understanding would be appreciated.

Sometimes, the speaker seemed not able to explain concepts concisely. For example, the explanation for DataFlow watermark:

"DataFlow then tracks whether that window is complete or not. And the check mark idea is it's complete. And the idea is that it's still receiving some events for, know the event time between 10 and 11. But it knows that, based on historical information that time window's unlikely to be complete and it needs to wait before it closes that window. ... DataFlow is learning this Watermark."

This could have been worded as "DataFlow determines whether it should close a window based on historical data. " I'm not sure how that "event time between 10 and 11" is relevant in the discussion.

Sometimes the explanation was about a different thing:

"the thing to remember is that in Bigquery, rows are sorted."

Actually the speaker was talking about Bigtable.

The speaker demonstrated preparation. That is a good thing.

교육 기관: Peter M

Feb 12, 2018

It teaches the basics but I am not convinced this course prepares the student for the Data Engineering Google Certification. For example, in the practice exam there are questions on topics that are not covered in this course. For example, IAM, Dataprep, Transfer Appliance, Storage Transfer Service, Cloud Bigtable cbt tool, Snapshots and Encryption. Some of the questions in the practice exam imply the student has taken Google Cloud Architect certification to cover topics omitted from the Data Engineer course given here. This anomaly should be addressed.

교육 기관: Daniel S

Dec 27, 2019

Lecture content was of good quality. The labs need significant work. Even if they were in Java, there could have been some effort taken to teach in the labs rather than copy/pasting terminal commands into a vm shell.

교육 기관: Bernardt D

May 24, 2018

Had some problems with opening multiple consoles and a '--description=...' param changed to something like '--display_name=...'

교육 기관: durgaprasad

Oct 02, 2019

I have gained some good amount knowledge on GCP with respect to Machine learning and stream data processing.

교육 기관: Kushal J

Nov 11, 2018

Qwiklabs lack of clarity in lab instructions spoiled the experience.