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PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python
Discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark.
PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python
Item #: 36765104

PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

Item #: 36765104

AED 227

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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

    Comprehensive Recipes
    Contains a wide variety of practical recipes designed to solve real-world problems, making it easy for both beginners and experts to enhance their PySpark skills effectively and efficiently.
    Expert Insights
    Written by seasoned professionals, this cookbook offers valuable insights and best practices that help users avoid common pitfalls while maximizing the potential of PySpark in big data projects.
    Hands-On Learning
    Encourages hands-on practice with clear examples and step-by-step instructions, ensuring readers not only learn PySpark concepts but also apply them confidently in their data processing tasks.

    Product Details

    Discover over 60 recipes for Apache Spark & Python, and learn how to effectively process & analyze big data. Shop now at Ubuy UAE.
    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?

    Suitable For
    • Data Scientists

      Ideal for data scientists looking to leverage PySpark for big data analysis and machine learning applications.

    • Software Engineers

      Beneficial for software engineers who want to integrate PySpark into existing applications for data processing.

    • Learning Enthusiasts

      Great for individuals eager to learn about big data technologies in a structured and easy-to-follow manner.

    Not Suitable For
    • 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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