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学生对 IBM 提供的 Introduction to Big Data with Spark and Hadoop 的评价和反馈

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465 个评分

课程概述

This self-paced IBM course will teach you all about big data! You will become familiar with the characteristics of big data and its application in big data analytics. You will also gain hands-on experience with big data processing tools like Apache Hadoop and Apache Spark. Bernard Marr defines big data as the digital trace that we are generating in this digital era. You will start the course by understanding what big data is and exploring how insights from big data can be harnessed for a variety of use cases. You’ll also explore how big data uses technologies like parallel processing, scaling, and data parallelism. Next, you will learn about Hadoop, an open-source framework that allows for the distributed processing of large data and its ecosystem. You will discover important applications that go hand in hand with Hadoop, like Distributed File System (HDFS), MapReduce, and HBase. You will become familiar with Hive, a data warehouse software that provides an SQL-like interface to efficiently query and manipulate large data sets. You’ll then gain insights into Apache Spark, an open-source processing engine that provides users with new ways to store and use big data. In this course, you will discover how to leverage Spark to deliver reliable insights. The course provides an overview of the platform, going into the components that make up Apache Spark. You’ll learn about DataFrames and perform basic DataFrame operations and work with SparkSQL. Explore how Spark processes and monitors the requests your application submits and how you can track work using the Spark Application UI. This course has several hands-on labs to help you apply and practice the concepts you learn. You will complete Hadoop and Spark labs using various tools and technologies, including Docker, Kubernetes, Python, and Jupyter Notebooks....

热门审阅

AA

Jan 15, 2024

Great program to explore more about AI and Big Data

JS

May 1, 2022

hands on lab and quizzes at the end of each session was very helpful

筛选依据：

51 - Introduction to Big Data with Spark and Hadoop 的 75 个评论（共 101 个）

创建者 Minh N T

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May 20, 2022

easy understanding

创建者 Phan N T A

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Oct 21, 2023

A great course!

创建者 Wilmer G N V

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May 20, 2025

good!!!!!!!!

创建者 Nkosikhona M

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Apr 4, 2024

Great course

创建者 Sabeur M

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Jan 22, 2024

great course

创建者 ANAGHA G K

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Sep 2, 2025

Nice course

创建者 Shivam K

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Feb 2, 2022

Good course

创建者 Tahmina H

•

Aug 2, 2025

Very nice!

创建者 Rubens T

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Jul 12, 2022

Excellent!

创建者 Juan C

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Dec 20, 2023

excelente

创建者 Tùng N P

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Feb 27, 2024

Great!

创建者 Sadhana G M

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Oct 14, 2023

USEFUL

创建者 Vaishnavi S

•

Oct 7, 2025

good

创建者 Le V T K

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Feb 22, 2024

good

创建者 PREM C N

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Nov 7, 2023

good

创建者 Nguyen V T F

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Jul 24, 2023

Nice

创建者 321910304004 g

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Apr 3, 2023

good

创建者 BARIGE S

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Mar 23, 2023

good

创建者 Sumit K

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Oct 15, 2022

wow

创建者 chukka A

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Jan 25, 2022

good

创建者 Sanket P

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Nov 5, 2025

Na

创建者 Vaishnavi M S

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Oct 1, 2025

.

创建者 Shazib S

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Sep 9, 2024

Excellent. But would have liked to have more detailed walk through on how to set-up (not just a reading) on my own computer and practice lab on that. Also, too many of the slides were just bullet points and could have been more visualized to ensure architectural themes are easily remembered (e.g. monitoring module was just textual and diagrams could have been more specific - especially linked with Sparks UI more closely than a few images after many bullet vis-a-vis nicely explained job/stage/task hierarchy and how it relates to client mode set-up.)

创建者 Rafael B

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Oct 25, 2024

Es un gran curso en donde he podido afianzar conceptos fundamentales tanto en Hadoop como en Spark. De no ser por estar absolutamente atado y dependiente de la plataforma en la nube de IBM, sería un curso perfecto. Me gustaría que en la primera parte del curso enseñaran cómo montar una plataforma localhost y luego pasar a conocer las ventajas de montar todo rápidamente en la muy buena plataforma de IBM.

创建者 Stefan U

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Oct 23, 2024

+ great IDE for hands-on labs + good intelligibility of computer-generated voice reading the texts - slightly boring data sets - some factual errors in the quizzes (e.g. radiobuttons instead of checkboxes for questions with multiple correct answers)