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Data Algorithms with Spark: Recipes and Design Patterns for Scaling Up using PySpark
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What Stands Out
Информация за продукта
| Publisher | O'Reilly Media |
| Publication date | May 17, 2022 |
| Edition | 1st |
| Language | English |
| Print length | 435 pages |
| ISBN-10 | 1492082384 |
| ISBN-13 | 978-1492082385 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 7 x 0.9 x 9.15 inches (17.8 x 2.3 x 23.2 cm) |
Who Should Buy?
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Data Engineers
Ideal for data engineers seeking to enhance their skills in data processing using Spark and PySpark techniques.
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Data Scientists
Useful for data scientists looking to implement scalable algorithms effectively with Spark in their workflows.
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Big Data Beginners
Great for individuals new to big data concepts who want practical recipes for using Spark with PySpark.
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Complete Beginners
Not suitable for absolute beginners without prior knowledge of programming or data processing concepts in Python.
ОПИСАНИЕ НА ПРОДУКТА
Data Algorithms with Spark: Recipes and Design Patterns for Scaling Up using PySpark
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Software Development Editorial Review
Data Algorithms with Spark: Recipes and Design Patterns for Scaling Up using PySpark is a comprehensive guide that dives deep into both Spark and PySpark architectures, providing practical data design patterns and algorithms for building robust Spark applications. Notably, the book begins with a thorough introduction to Spark, covering essential concepts while addressing advanced aspects like RDD and DataFrame operations. Readers have praised the writing style of Prof. Parsian, highlighting the clear presentation and helpful examples that relate closely to real-world industry practices. This makes the book an invaluable resource for anyone looking to enhance their skills in data analytics and big data engineering.
Customer Reviews & Ratings
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Професионалисти
- Well-organized and clearly presented content
- In-depth practical examples for PySpark
- Covers both RDDs and DataFrames effectively
- Great for beginners in big data analytics
- Provides solutions and GitHub examples for problems
минуси
- Some patterns may appear obvious but are still useful.
Platform Trust & Buyer Confidence
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