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Databricks Certified Associate Developer for Apache Spark Using Python

You're reading from   Databricks Certified Associate Developer for Apache Spark Using Python The ultimate guide to getting certified in Apache Spark using practical examples with Python

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781804619780
Length 274 pages
Edition 1st Edition
Languages
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Author (1):
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Saba Shah Saba Shah
Author Profile Icon Saba Shah
Saba Shah
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1: Exam Overview
2. Chapter 1: Overview of the Certification Guide and Exam FREE CHAPTER 3. Part 2: Introducing Spark
4. Chapter 2: Understanding Apache Spark and Its Applications 5. Chapter 3: Spark Architecture and Transformations 6. Part 3: Spark Operations
7. Chapter 4: Spark DataFrames and their Operations 8. Chapter 5: Advanced Operations and Optimizations in Spark 9. Chapter 6: SQL Queries in Spark 10. Part 4: Spark Applications
11. Chapter 7: Structured Streaming in Spark 12. Chapter 8: Machine Learning with Spark ML 13. Part 5: Mock Papers
14. Chapter 9: Mock Test 1
15. Chapter 10: Mock Test 2
16. Index 17. Other Books You May Enjoy

Repartitioning and coalescing in Apache Spark

Efficient data partitioning plays a crucial role in optimizing data processing workflows in Apache Spark. Repartitioning and coalescing are operations that allow you to control the distribution of data across partitions. In this section, we’ll explore the concepts of repartitioning and coalescing and their significance in Spark applications.

Understanding data partitioning

Data partitioning in Apache Spark involves dividing a dataset into smaller, manageable units called partitions. Each partition contains a subset of the data and is processed independently by different worker nodes in a distributed cluster. Proper data partitioning can significantly impact the efficiency and performance of Spark applications.

Repartitioning data

Repartitioning is the process of redistributing data across a different number of partitions. This operation can help balance data distribution, improve parallelism, and optimize data processing...

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