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PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python
88% of respondents would recommend this to a friend
CVE 6722
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Discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark.
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What Stands Out
Product Details
| Publisher | Packt Publishing |
| Publication date | June 29, 2018 |
| Language | English |
| Print length | 330 pages |
| ISBN-10 | 1788835360 |
| ISBN-13 | 978-1788835367 |
| Item Weight | 1.25 pounds (570 grams) |
| Dimensions | 7.5 x 0.75 x 9.25 inches (19.1 x 1.9 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to leverage PySpark for big data analysis and machine learning applications.
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Software Engineers
Beneficial for software engineers who want to integrate PySpark into existing applications for data processing.
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Learning Enthusiasts
Great for individuals eager to learn about big data technologies in a structured and easy-to-follow manner.
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Beginner Programmers
Not suitable for beginners in programming who haven't yet grasped data manipulation concepts and frameworks.
Product Description
PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python
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Data Modeling & Design Editorial Review
The customer feedback on this Spark-related product paints a rather mixed picture, leaning heavily towards disappointment among readers who were eager to learn the intricacies of PySpark and improve their skills for the Databricks Associate Spark Developer certification. While some reviewers did appreciate the author's approach and the intention behind the text, a significant concern arose regarding the Kindle version's graphics, which many found to be illegible regardless of how they attempted to view or magnify them. This lack of clarity significantly hindered the learning experience. Furthermore, claims of plagiarism were flagged by some customers, suggesting that the content of the book lacks originality and relies overly on material readily available from Wikipedia and other open sources. Many readers reported frustration with the inadequacy of the explanations surrounding the code, making the book seem like a poor investment compared to alternative resources. Readers who sought a well-structured guide into Spark quickly turned to recommended titles from Databricks and appreciated other more comprehensive materials from reputable publishers like O'Reilly. The Consensus appeared to indicate that “PySpark Cookbook” does not live up to expectations in terms of both content and value, overshadowed by better available options. **
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Pros
- Some found the author's approach commendable.
- A few readers identified Chapter 4 as containing useful data-manipulation tasks.
Cons
- Accusations of plagiarism and lack of original content.
Product Price History
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CVE 6722
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Features & Benefits
- Learn to efficiently process data using PySpark.
- Gain insights into machine learning with Apache Spark and Python.
- Explore hands-on recipes for building big data applications.
- Master the use of RDDs and DataFrames for data abstraction.
- Deploy applications to the cloud seamlessly using Spark.
- Ideal for Python developers looking to enhance their big data skills.
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