Data Analysis with Python and PySpark
Data Analysis with Python and PySpark
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Data Analysis with Python and PySpark
Data Analysis with Python and PySpark
PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines.
Data Analysis with Python and PySpark
Stavka #: 123736829

Data Analysis with Python and PySpark

85% of respondents would recommend this to a friend

Stavka #: 123736829

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PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines.
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What Stands Out

Comprehensive Coverage
Offers an extensive overview of data analysis concepts, integrating Python and PySpark, making it suitable for both beginners and experienced practitioners seeking to enhance their skills.
Practical Examples
Includes hands-on examples and case studies, allowing users to apply theoretical knowledge to real-world data challenges, effectively bridging the gap between learning and implementation.
Up-to-Date Techniques
Incorporates the latest advancements in data analysis and big data frameworks, ensuring that readers are equipped with current tools and methodologies critical in today's data-driven environment.

Detalji o proizvodu

Shop Data Analysis with Python and PySpark online at a best price in Bosnia and Herzegovina. 1617297208
  • When it comes to data analytics, it pays tothink big. PySpark blends the powerful Spark big data processing engine withthe Python programming language to provide a data analysis platform that can scaleup for nearly any task. Data Analysis with Python and PySpark is yourguide to delivering successful Python-driven data projects. Data Analysis with Python and PySpark is a carefully engineered tutorial that helps you use PySpark to deliver your data-driven applications at any scale. This clear and hands-on guide shows you how to enlarge your processing capabilities across multiple machines with data from any source, ranging from Had oop-based clusters to Excel worksheets. You'll learn how to break down big analysis tasks into manageable chunks and how to choose and use the best PySpark data abstraction for your unique needs. The Spark data processing engine is an amazing analytics factory: raw data comes in,and insight comes out. Thanks to its ability to handle massive amounts of data distributed across a cluster, Spark has been adopted as standard by organizations both big and small. PySpark, which wraps the core Spark engine with a Python-based API, puts Spark-based data pipelines in the hands of programmers and data scientists working with the Python programming language. PySpark simplifies Spark's steep learning curve, and provides a seamless bridge between Spark and an ecosystem of Python-based data science tools.
Publisher Manning Publications
Publication date 16 Mar. 2022
Edition 1st
Language English
Print length 425 pages
ISBN-10 1617297208
ISBN-13 978-1617297205
Item weight 210 g
Dimensions 18.75 x 2.9 x 23.5 cm

Who Should Buy?

Suitable For
  • Beginner Data Analysts

    Perfect for newcomers wanting to learn data analysis using Python and PySpark through hands-on examples and exercises.

  • Data Science Students

    Ideal for university students seeking a comprehensive guide to develop their data analysis skills using modern tools.

  • Professionals Upskilling

    Great for working professionals aiming to enhance their data analytics capabilities and employ Spark in their projects.

Not Suitable For
  • Advanced Practitioners

    Not suitable for experienced data scientists or analysts looking for advanced techniques or cutting-edge methodologies.

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Značajke i prednosti

  • Seamlessly scale up data tasks with PySpark
  • Read and write data from various sources and formats
  • Build automated data pipelines for transformation and insights
  • Practical book with relevant examples and quick exercises
  • No previous knowledge of Spark required
  • Ideal for data scientists and data engineers comfortable with Python

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