学生对 EDUCBA 提供的 PySpark & Python: Hands-On Guide to Data Processing 的评价和反馈
课程概述
热门审阅
AA
Dec 6, 2025
I also appreciated the explanations around performance tuning and optimization basics, which many beginner courses often skip.
FB
Oct 20, 2025
I’ve taken many courses before, but this one stands out for its practical approach to PySpark. Real examples made all the difference. Highly recommended for professionals.
1 - PySpark & Python: Hands-On Guide to Data Processing 的 25 个评论(共 35 个)
创建者 karolynmcrae
•Nov 29, 2025
Overall, this course is a valuable guide for anyone wanting to learn data processing with PySpark and Python—practical, beginner-friendly, and well-paced for real-world learning.
创建者 freddie b
•Oct 21, 2025
I’ve taken many courses before, but this one stands out for its practical approach to PySpark. Real examples made all the difference. Highly recommended for professionals.
创建者 Devendra F
•Oct 28, 2025
The best PySpark course I’ve taken! The instructor’s explanations, examples, and projects are all top-notch. It’s practical, beginner-friendly, and industry-relevant.
创建者 danette b
•Oct 26, 2025
The instructor provides great insights into distributed computing and real-life data workflows. Ideal for anyone looking to level up in data engineering.
创建者 Teena M
•Oct 14, 2025
If you want to master PySpark data processing from scratch, this course is your best bet! Clear concepts and hands-on coding make it valuable.
创建者 David J
•Nov 9, 2025
The course explains PySpark concepts in a very practical and approachable way, making it easier to understand large-scale data processing.
创建者 armidameier
•Dec 6, 2025
I also appreciated the explanations around performance tuning and optimization basics, which many beginner courses often skip.
创建者 sumit j
•Oct 29, 2025
I learned so much about PySpark architecture, transformations, and actions. Ideal for anyone stepping into data engineering.
创建者 Danna B
•Oct 18, 2025
I was impressed by how interactive and engaging this course is. The instructor makes learning PySpark genuinely enjoyable.
创建者 Surendranath B
•Nov 6, 2025
This course turned my confusion about PySpark into complete understanding. A great investment for data professionals!
创建者 Maahi N
•Oct 27, 2025
Insightful but somewhat basic; lacks depth and advanced techniques for seasoned PySpark and Python professionals.
创建者 Sunita W
•Nov 15, 2025
Topics progress naturally—from basic operations to more advanced transformations—without overwhelming beginners.
创建者 nannettemetz
•Dec 14, 2025
It helps learners understand how big data processing differs from traditional single-machine processing.
创建者 artiemeeks
•Oct 4, 2025
Practical and clear guide with hands-on examples, great for learning PySpark and Python data processing.
创建者 Krishnachandra P
•Nov 10, 2025
I can now write efficient PySpark pipelines confidently. This course truly delivers on its promises.
创建者 Vishwanath V
•Nov 5, 2025
The course for mastering PySpark and Python data workflows—clear explanations and real projects!
创建者 carleenmayes
•Dec 27, 2025
Fantastic course if you want to go beyond theory and actually do data processing with PySpark.
创建者 Archana N
•Oct 27, 2025
really good and helpful instructor, content was good and examples were helpful to walk through
创建者 Narendranath D
•Nov 12, 2025
Each topic builds naturally, making it perfect for beginners and intermediate learners alike.
创建者 Ankita k
•Nov 8, 2025
Ideal for professionals aiming to scale up in data engineering. Well worth the investment.
创建者 Muthuswamy I
•Nov 1, 2025
The instructor’s explanations made even the toughest PySpark topics manageable.
创建者 Wasim S
•Oct 23, 2025
It was a great class, really engaging.
创建者 Round P
•Oct 17, 2025
The course covers all essential topics
创建者 Noman A
•Oct 19, 2025
What an excellent instructor
创建者 joellen m
•Nov 22, 2025
On the downside, some parts feel a bit surface-level. A few topics like optimization, Spark internals, or real-world ETL pipelines aren’t covered in much depth. The hands-on sections are good, but they could have included more complex examples or larger datasets to show Spark’s true strengths.