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學生對 加州大学圣地亚哥分校 提供的 Big Data Integration and Processing 的評價和反饋

4.4
2,140 個評分
457 條評論

課程概述

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+....

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SB
2020年10月21日

Hello Gentlemen,\n\nThis course was very helpful foe me. It enhanced my knowledge about Big Data Integration. Thank you so much for providing me such important knowledge. Thank you once again.

AA
2018年3月5日

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.

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126 - Big Data Integration and Processing 的 150 個評論(共 444 個)

創建者 roopesh d

2017年8月22日

very nice. easy to understand and great hands on exercises.

創建者 PAMELA D

2018年9月25日

This was very challenging and very rewarding. What fun wra

創建者 Haozhe ( X

2020年11月10日

Great intro course for big data with HDFS and integration

創建者 Ravi V A

2020年5月7日

This course was designed very well with practical info.

創建者 Leandro R

2017年5月12日

Excellent course and very demanding! I really enjoyed.

創建者 Yogesh P

2018年3月27日

Good Course covering many concepts and fundamentals.

創建者 ZIKIF H L

2017年2月5日

Lots of learning of programming Spark and MongoDB

創建者 Miguel d l S T

2017年1月12日

interesting course, I learned a lot about MongoDB

創建者 Shipra G

2018年4月27日

One of the best course fro beginners in Big data

創建者 An D

2017年4月2日

Thank you. Spark in this course is very helpful.

創建者 EDGARD M M

2017年2月13日

Very good course and professors. I recommend it.

創建者 DHANRAJ N

2019年7月11日

This course is very interactive and practical.

創建者 Srishti R

2019年3月4日

Great experience towards learning this course

創建者 Nick B

2018年11月28日

Enjoyed the class. It was very enlightening.

創建者 Darius T

2018年1月1日

Great course for learning MongoDB and pySpark

創建者 SARSAR M E M

2020年7月29日

very interesting course for big data fans :)

創建者 Zhi Q

2017年1月12日

a wide spectrum of tools practiced. awesome!

創建者 Assaduzzaman N

2017年9月26日

This is a very good course in most aspects.

創建者 SNEHADEEP Y S

2020年6月5日

Excellant Great exeperince learning course

創建者 Tanuj P

2018年2月28日

Good assignments at the end of the course.

創建者 AISHWARYA V

2020年5月26日

Very interesting and informative course.

創建者 Jose L R

2018年10月13日

Great course! Please update the HandsOn.

創建者 Rajesh P

2017年11月9日

Very good course for understanding spark

創建者 Ian M

2017年2月2日

Lots of good hands-on and explanation...

創建者 Suhail M

2020年7月16日

Need More Hands on except that all good