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Item #:
123736829
Data Analysis with Python and PySpark
85% of respondents would recommend this to a friend
Item #:
123736829
TTD 492
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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.
Product Details
- 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?
-
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.
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Professionals Upskilling
Great for working professionals aiming to enhance their data analytics capabilities and employ Spark in their projects.
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Advanced Practitioners
Not suitable for experienced data scientists or analysts looking for advanced techniques or cutting-edge methodologies.
Product Description
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Data Mining Editorial Review
Data Analysis With Python And PySpark is an exceptional resource that effectively introduces readers to the intricacies of PySpark and its integration with Python. The book, published by Manning Publications, spans 425 pages and is well-structured for newcomers to data analysis. Readers appreciate the clear explanations and practical techniques, which build confidence for tackling data frames and machine learning with large datasets. It successfully guides individuals from beginner to advanced levels, making it highly recommended for those looking to enhance their data analytics skills.
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Pros
- Well-written and easy to understand for beginners
- Comprehensive coverage of key PySpark concepts
- Practical techniques for data analytics pipelines
- Guides readers from basics to advanced skills
- Great resource for both Python and Spark integration
Cons
- Primarily focused on beginners; advanced users may seek more depth
Product Price History
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Features & Benefits
- 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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