Chevron Left
Volver a Big Data Integration and Processing

Opiniones y comentarios de aprendices correspondientes a Big Data Integration and Processing por parte de Universidad de California en San Diego

4.4
estrellas
1,770 calificaciones
376 revisiones

Acerca del Curso

At the end of the course, you will be able to: *Retrieve data from example database and big data management systems *Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications *Identify when a big data problem needs data integration *Execute simple big data integration and processing on Hadoop and Spark platforms This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+....

Principales revisiones

FC

Sep 25, 2016

Best course taking into account the first three. Good material, more in depth than the other ones. Very well explained. Useful to get a sense of various interesting topics and orientative.

AA

Mar 06, 2018

It was a good course, it could have been better if some examples of Spark were also provided in other Languages like Java, people without having background of python may find it difficult.

Filtrar por:

226 - 250 de 365 revisiones para Big Data Integration and Processing

por Damon A

Aug 08, 2017

I enjoyed the practical introduction to Hadoop and Spark. I appreciated the quizzes, which exposed me to both platforms via hands on experience.

por Vidal A C L

Oct 23, 2017

Challenging hands on experience working with both structured and unstructured data, and creating value out of it by using SPARK and MongoDB.

por Pankaj G

Dec 05, 2016

Content was good however we need to get more support on hands on as sometime we feel that we are stuck due to non availability of the right

por Rohit G

Jul 09, 2019

Environment setup instructions for final assignment do not work as expected and need to be updated to avoid time spent in troubleshooting.

por Mahmudul H

May 23, 2020

All of a sudden this course is enriched with hands on practice. I think it should be more improved as everything is not clear that much.

por Guilherme D C T

May 13, 2019

The final project is a bit tough but worth it. If you manage to finish it you'll have a new understanding of Spark RDDs and DataFrames.

por Billu

Dec 21, 2018

Technical support for hands-on work is ridiculously out of date at the rare occasion it is available. and i’m paying for this course!

por ISLAM K

Mar 26, 2018

this course is great , its giving many hands-on sessions that allow the student to be on the way of being professional in this career

por Gnana P B

Oct 15, 2019

It is good course. Some instructions followed not working, course instructions and supplied resources requires update. thank you

por Gopi N

Oct 30, 2018

Need more hands-on for final test, would really help to have extra two weeks split with just good handson. Before final test

por Omprakash S

Jan 16, 2018

More Hands on experience should be included.

Reading of Apache Spark documentation should be made mandatory for beginners.

por Rafael T P d S

Mar 04, 2019

The last quiz was very hard to complete. I didn't found enough content to solve que questions in the course material.

por Samarth S

Apr 11, 2018

Need more explainaion. There are few videos such as that of RDD where I found difficulty in understanding the concept

por Jyothi-Raghav J

Nov 02, 2017

Except for the exercise where it was frustrating to get the Spark Streaming to work, it was a very good learning.

por Mohamed

Sep 17, 2017

Need a references for any one want to read more about each subject. I recommend have it at the end of each Week.

por Prashant K

Oct 15, 2017

Helpful course to learn the basic components and processing on Apache Spark. Course quiz for MongoDB is great.

por Bhola N R

Dec 04, 2018

The assignments for Week6 was really tough, request to provide more hints or support in completing the same.

por Juan J S O

Dec 01, 2018

I learnt a lot in this course, but is hard. Many times the instructions in the assignments were not enough.

por Manish S

Feb 10, 2018

For better practice, more question sets should be provided apart from the quizzes that we already have.

por Devi V V

Jan 23, 2017

course is very good. Excercises are very good. learnt alot about the mongodb and spark sql and RDD's

por Gustavo J

Nov 16, 2017

This is mre interesting than the previous courses on the Big Data Specialization. More hands on...

por Ahmad M E R

Mar 01, 2019

The most interesting part in this course dealing with spark and the final quiz is really amazing

por Cordell M

Jul 22, 2017

that last spark exam was really hard. the script took a long time to finally get working

por Xiuting W

Mar 25, 2019

Useful course. Give more examples of small projects would make this course better.

por Benh L S

Feb 27, 2020

Very good, but a few glitches with the instructions to setup PySpark in my view.