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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: 1,154
Рецензии: 151

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
27 мая 2020 г.

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 июля 2020 г.

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

Фильтр по:

1–25 из 152 отзывов о курсе Building Batch Data Pipelines on GCP

автор: RLee

12 февр. 2020 г.

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

24 янв. 2020 г.

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

2 февр. 2020 г.

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

24 апр. 2020 г.

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

1 апр. 2020 г.

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

17 апр. 2020 г.

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

автор: Léo Z

27 февр. 2020 г.

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

автор: Xavier A A

6 мая 2020 г.

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

6 февр. 2020 г.

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

2 мая 2020 г.

The Labs need to be tested

автор: Thomas M

6 янв. 2021 г.

Its obvious some of the narators don't even understand the subjects they are talking about because they can't pronounce the words correctly. Come on. Let's at least read the scripts a few times before getting in front the camera. In this whole course sofar there's only two narators who obviously have played around with this technology. The rest just read the scripts which is so boring to listen to. I can read the scripts myself. Just smiling doesn't cut it. You need to understand what you are talking about so you accent things properly and sound like an expert.

автор: Johnny C

3 мая 2020 г.

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

12 апр. 2020 г.

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

автор: Hendra D S C

10 апр. 2020 г.

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

автор: Mahmoud M

5 апр. 2020 г.

Quite hard to follow

автор: g m

2 окт. 2020 г.

Do not waste your time doing this course/specialisation. The labs are extremely buggy and do not fully work making this course similar to studying chemistry without ever doing a single lab; instead the labs are just described to you. The video lectures are good and could be better the pace of the lectures was a little quicker - which you can adjust yourself if needed.

I'm going to see if I can cancel my subscription to this dodgy specialisation - a first for my time with Coursera!

автор: Yuri M

8 сент. 2020 г.

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

26 апр. 2020 г.

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

21 авг. 2020 г.

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

28 июня 2020 г.

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

23 мая 2020 г.

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

21 мая 2020 г.

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

11 апр. 2020 г.

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

28 мая 2020 г.

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

17 июля 2020 г.

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