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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

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781804619780

Length 274 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Data Engineering

Author (1):

Saba Shah

View More author details

Table of Contents (18) Chapters

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

Why subscribe?

17. Other Books You May Enjoy

UDFs in Apache Spark

UDFs are a powerful feature in Apache Spark that allows you to extend the functionality of Spark by defining custom functions. UDFs are essential for transforming and manipulating data in ways not directly supported by built-in Spark functions. In this section, we’ll delve into the concepts, implementation, and best practices for using UDFs in Spark.

What are UDFs?

UDFs are custom functions that are created by users to perform specific operations on data within Spark. UDFs extend the range of transformations and operations you can apply to your data, making Spark more versatile for diverse use cases.

Here are some of the key characteristics of UDFs:

User-customized logic: UDFs allow you to apply user-specific logic or custom algorithms to your data
Support for various languages: Spark supports UDFs written in various programming languages, including Scala, Python, Java, and R
Compatibility with DataFrames and resilient distributed...

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Authors (1)

Saba Shah

Saba Shah

Saba Shah is a Data and AI Architect and Evangelist with a wide technical breadth and deep understanding of big data and machine learning technologies. She has experience leading data science and data engineering teams in Fortune 500s as well as startups. She started her career as a software engineer but soon transitioned to big data. She is currently a solutions architect at Databricks and works with enterprises building their data strategy and helping them create a vision for the future with machine learning and predictive analytics. Saba graduated with a degree in Computer Science and later earned an MS degree in Advanced Web Technologies. She is passionate about all things data and cricket. She currently resides in RTP, NC.

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