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Вернуться к Building Batch Data Pipelines on GCP

Отзывы учащихся о курсе Building Batch Data Pipelines on GCP от партнера Google Cloud

4.5
звезд
Оценки: 1,026
Рецензии: 133

О курсе

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 Platform for data transformation including BigQuery, executing Spark on Cloud Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Cloud Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud Platform using QwikLabs. New! CERTIFICATE COMPLETION CHALLENGE to unlock benefits from Coursera and Google Cloud Enroll and complete Cloud Engineering with Google Cloud or Cloud Architecture with Google Cloud Professional Certificate or Data Engineering with Google Cloud Professional Certificate before November 8, 2020 to receive the following benefits; => Google Cloud t-shirt, for the first 1,000 eligible learners to complete. While supplies last. > Exclusive access to Big => Interview ($950 value) and career coaching => 30 days free access to Qwiklabs ($50 value) to earn Google Cloud recognized skill badges by completing challenge quests...

Лучшие рецензии

UB

May 28, 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 17, 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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1–25 из 133 отзывов о курсе Building Batch Data Pipelines on GCP

автор: RLee

Feb 12, 2020

More teaching should be focused on how to build the python file of each task, rather than ready for us to run.

автор: Roger S P M

Jan 24, 2020

Google did not work very hard to convey this information in its lectures. They are just bullet slides with a talking head. They need to learn how better course developers are creating courses.

автор: Tamás-Marosi P

Feb 02, 2020

There were some minor problem and mistake in the lab file. The python/java scripts were not explained at all. There are questions about the code itself, but then the questions were not answered.

автор: Prashanth T

Apr 25, 2020

I'm not from a programming (Java/.NET/Python etc) background but Informatica ETL, Oracle PL/SQL and UNIX. I wish the code as part of Serverless Data Analysis with Dataflow: Side Inputs (Python) explained step by step like where it starts and then step by step. If it sounds redundant, a document reference to each step would help.

автор: James W

Apr 01, 2020

Great addition to the Data Engineering line up. In addition to the updated content on newer tools like Cloud Composer and Data Fusion, I feel like the detail on tools like Dataproc and Dataflow is better than it used to be.

автор: Rahul J S

Apr 18, 2020

questions given in the quiz are pretty simple.. need those questions which can ensure learners to brainstorm

автор: Léo Z

Feb 27, 2020

Informative and interactive course introducing to DataProc, DataFlow, Apache Beam, Apache Airflow.

автор: Xavier A A

May 06, 2020

Last few videos in Week2 have too much python, the only relief is that we have templates to save time. I have to spend more time in understanding the python scripts used in the last 4-5 videos. I have worked with python only for wrangling data using pandas, only grep.py & grepc.py were easy, all others were deep for me to understand. Maybe the course has to reiterate the importance of python programming. What if I want to build it custom, templates may not help. So, please stress the importance of python/java skills in the course

автор: Scott P

Feb 07, 2020

There were too many labs with services that take 30-40 minutes just to spin up. I wouldn't have a problem with all the labs if the services took 2-5 minutes to spin up.

автор: Adolfo C Y

May 03, 2020

The Labs need to be tested

автор: Johnny C

May 03, 2020

a lot to digest on this course... pace is too high in my opinion, it should be slower. good course anyways.

thanks

автор: Divyangana P

Apr 12, 2020

Dataflow Labs should be explained in greater detail so as to provide comprehensive learning

автор: Hendra D S C

Apr 10, 2020

Some labs could be better in term of the thinking rationale, error handling, etc.

автор: Mahmoud M

Apr 05, 2020

Quite hard to follow

автор: Yuri M

Sep 08, 2020

1 punto

Case studies in this course are intended to develop the skill of defining the solution while analyzing the circumstance. This is a key test-taking and job skill.

Practice Exam Questions help develop the skill of being aware of how certain you are of an answer. This is not only a test-taking skill and a job skill, but also helps you understand where you may want to study more to prepare.

This course provided an exhaustive list of basic principles and concepts and tested you repeatedly on your ability to remember them.

This course introduced "touchstone" concepts that are based on many fundamental concepts. If you don't feel confident about a "touchstone" concept, it is an indicator that you might want to study the underlying concepts and technologies.

2.

автор: Jeanmann P

Apr 26, 2020

Course was great. Easy to understand and has many labs to try. One issue was that the first Dataflow lab was not working due to the Apache Beam issue. I worked with a rep and he said he would follow up with me after resolving the issue. But he never contacted me again. Probably the issue was nor resolved. So I never completed that lab.

автор: James H

Aug 21, 2020

I learned how to manage GCP services to process big data, including the usage of cluster computing services, and serverless computing. I additionally learned how to build batch data pipelines, including creation of visual data pipelines, learn about Python code to process data, and the architecture of a batch data pipeline.

автор: ARVIND K S

Jun 28, 2020

An excellent course imparting tremendous knowledge and skills in several technologies for data transformation, executing Spark and Hadoop with Dataproc and serverless Dataflow. It's a great sense of achievement to actually build data pipelines in various course projects.

автор: Iman R

May 23, 2020

Great course. This course tell about developing pipeline for various situation, so the learned can gain the experience in the field too. Because sometime I think just doing it at the course lab and gain the experience from it, it's just not enought

автор: Gaurav S

May 21, 2020

It was really good. However, could be better if it has more in-depth practice labs when it comes to core concepts of cloud functions, Ptransforms, Par-Dos, Pcollections , side inputs, Maps vs Flat maps and combine

автор: Ivan E K

Apr 11, 2020

It is an excellent course, I really recommend it. It gives very specific information, very technical, which is ultimately what you need when things get difficult or to take the certification exam.

автор: Uday K B

May 28, 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.

автор: Alejandro D

Jul 17, 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

автор: Aniruddha S

May 20, 2020

Informative on various features. But cloud fusion and dataflow are not very clearly explained in detail.. expecting more on this. Want to learn more on the pipeline topic please.

автор: Sankara S

Jun 19, 2020

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.