Databricks Certified Associate Developer for Apache Spark Using Python: The ultimate guide to getting certified in Apache Spark using practical examples with Python

Databricks Certified Associate Developer for Apache Spark Using Python

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To my parents, Neelam Khalid (late) and Syed Khalid Mahmood, for their sacrifices and for exemplifying the power of patience and determination. To my loving husband, Arslan Shah, for being by my side through the ups and downs of life and being my support through it all. To Amna Shah for being my sister and friend. To Mariam Wasim for reminding me what true friendship looks like.

– Saba Shah

Foreword

I have known and worked with Saba Shah for several years. Saba’s journey with Apache Spark began about 10 years ago. In this book, she will guide readers through the experiences she has gained on her journey.

In today’s dynamic data landscape, proficiency in Spark has become indispensable for data engineers, analysts, and scientists alike. This guide, meticulously crafted by seasoned experts, is your key to mastering Apache Spark and achieving certification success.

The journey begins with an insightful overview of the certification guide and exam, providing invaluable insights into what to expect and how to prepare effectively. From there, Saba delves deep into the core concepts of Spark, exploring its architecture, transformations, and the myriad of applications it enables.

As you progress through the chapters, you’ll gain a comprehensive understanding of Spark DataFrames and their operations, paving the way for advanced techniques and optimization strategies. From adaptive query execution to structured streaming, each topic is meticulously dissected, ensuring you gain a thorough grasp of Spark’s capabilities.

Machine learning enthusiasts will find a dedicated section on Spark ML, empowering them to harness the power of Spark for predictive analytics and model development. Additionally, two mock tests serve as the ultimate litmus test, allowing you to gauge your readiness and identify areas for improvement.

Whether you’re embarking on your Spark journey or seeking to validate your expertise with certification, this guide equips you with the knowledge, tools, and confidence needed to excel. Let this book be your trusted companion as you navigate the complexities of Apache Spark and embark on a journey of continuous learning and growth.

With Saba’s words, step-by-step instructions, screenshots, source code snippets, examples, and links to additional sources of information, you will learn how to continuously enhance your skills and be well-equipped to be a certified Apache Spark developer.

Best wishes on your certification journey!

Rod Waltermann

Distinguished Engineer

Chief Architect Cloud and AI Software

Lenovo

Contributors

About the author

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 at Fortune 500 firms as well as start-ups. 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. She currently resides in Research Triangle Park, North Carolina. In this book, she shares her expertise to empower you in the dynamic world of Spark.

About the reviewers

Aviral Bhardwaj is a professional with six years of experience in the big data domain, showcasing expertise in technologies such as AWS and Databricks. Aviral has collaborated with companies including Knowledge Lens, ZS Associates, Amgen Inc., AstraZeneca, Lovelytics, and FanDuel as a contractor, and he currently works with GMG Inc. Furthermore, Aviral holds certifications as a Databricks Certified Spark Associate, Data Engineer Associate, and Data Engineering Professional, demonstrating a deep understanding of Databricks.

Rakesh Dey is a seasoned data engineer with eight years of experience in total and six years of experience in different big data technologies, such as Spark, Hive, and Impala. He has extensive knowledge of the Databricks platform to build any end-to-end ETL project implementation. He has worked on different projects with new technologies and helped customers to achieve performance and cost optimization relative to on-premises solutions. He has different Databricks certifications from the intermediate to professional levels. He currently works at Deloitte.

Table of Contents

Preface

Part 1: Exam Overview

1

Overview of the Certification Guide and Exam

Overview of the certification exam

Distribution of questions

Resources to prepare for the exam

Resources available during the exam

Registering for your exam

Prerequisites for the exam

Online proctored exam

Types of questions

Theoretical questions

Code-based questions

Summary

Part 2: Introducing Spark

2

Understanding Apache Spark and Its Applications

What is Apache Spark?

The history of Apache Spark

Understanding Spark differentiators

The components of Spark

Why choose Apache Spark?

Speed

Reusability

In-memory computation

A unified platform

What are the Spark use cases?

Big data processing

Machine learning applications

Real-time streaming

Graph analytics

Who are the Spark users?

Data analysts

Data engineers

Data scientists

Machine learning engineers

Summary

Sample questions

3

Spark Architecture and Transformations

Spark architecture

Execution hierarchy

Spark components

Spark driver

SparkSession

Cluster manager

Spark executors

Partitioning in Spark

Deployment modes

RDDs

Lazy computation

Transformations

Summary

Sample questions

Answers

Part 3: Spark Operations

4

Spark DataFrames and their Operations

Getting Started in PySpark

Installing Spark

Creating a Spark session

Dataset API

DataFrame API

Creating DataFrame operations

Using a list of rows

Using a list of rows with schema

Using Pandas DataFrames

Using tuples

How to view the DataFrames

Viewing DataFrames

Viewing top n rows

Viewing DataFrame schema

Viewing data vertically

Viewing columns of data

Viewing summary statistics

Collecting the data

Using take

Using tail

Using head

Counting the number of rows of data

Converting a PySpark DataFrame to a Pandas DataFrame

How to manipulate data on rows and columns

Selecting columns

Creating columns

Dropping columns

Updating columns

Renaming columns

Finding unique values in a column

Changing the case of a column

Filtering a DataFrame

Logical operators in a DataFrame

Using isin()

Datatype conversions

Dropping null values from a DataFrame

Dropping duplicates from a DataFrame

Using aggregates in a DataFrame

Summary

Sample question

Answer

5

Advanced Operations and Optimizations in Spark

Grouping data in Spark and different Spark joins

Using groupBy in a DataFrame

A complex groupBy statement

Joining DataFrames in Spark

Reading and writing data

Reading and writing CSV files

Reading and writing Parquet files

Reading and writing ORC files

Reading and writing Delta files

Using SQL in Spark

UDFs in Apache Spark

What are UDFs?

Creating and registering UDFs

Use cases for UDFs

Best practices for using UDFs

Optimizations in Apache Spark

Understanding optimization in Spark

Catalyst optimizer

Adaptive Query Execution (AQE)

Data-based optimizations in Apache Spark

Addressing the small file problem in Apache Spark

Tackling data skew in Apache Spark

Managing data spills in Apache Spark

Managing data shuffle in Apache Spark

Shuffle joins

Shuffle sort-merge joins

Broadcast joins

Broadcast hash joins

Narrow and wide transformations in Apache Spark

Narrow transformations

Wide transformations

Choosing between narrow and wide transformations

Optimizing wide transformations

Persisting and caching in Apache Spark

Understanding data persistence

Caching data

Unpersisting data

Best practices

Repartitioning and coalescing in Apache Spark

Understanding data partitioning

Repartitioning data

Coalescing data

Use cases for repartitioning and coalescing

Best practices

Summary

Sample questions

Answers

6

SQL Queries in Spark

What is Spark SQL?

Advantages of Spark SQL

Integration with Apache Spark

Key concepts – DataFrames and datasets

Getting started with Spark SQL

Loading and saving data

Utilizing Spark SQL to filter and select data based on specific criteria

Exploring sorting and aggregation operations using Spark SQL

Grouping and aggregating data – grouping data based on specific columns and performing aggregate functions

Advanced Spark SQL operations

Leveraging window functions to perform advanced analytical operations on DataFrames

User-defined functions

Working with complex data types – pivot and unpivot

Summary

Sample questions

Answers

Part 4: Spark Applications

7

Structured Streaming in Spark

Real-time data processing

What is streaming?

Streaming architectures

Introducing Spark Streaming

Exploring the architecture of Spark Streaming

Key concepts

Advantages

Challenges

Introducing Structured Streaming

Key features and advantages

Structured Streaming versus Spark Streaming

Limitations and considerations

Streaming fundamentals

Stateless streaming – processing one event at a time

Stateful streaming – maintaining stateful information

The differences between stateless and stateful streaming

Structured Streaming concepts

Event time and processing time

Watermarking and late data handling

Triggers and output modes

Windowing operations

Joins and aggregations

Streaming sources and sinks

Built-in streaming sources

Custom streaming sources

Built-in streaming sinks

Custom streaming sinks

Advanced techniques in Structured Streaming

Handling fault tolerance

Handling schema evolution

Different joins in Structured Streaming

Stream-stream joins

Stream-static joins

Final thoughts and future developments

Summary

8

Machine Learning with Spark ML

Introduction to ML

The key concepts of ML

Types of ML

Types of supervised learning

ML with Spark

Advantages of Apache Spark for large-scale ML

Spark MLlib versus Spark ML

ML life cycle

Problem statement

Data preparation and feature engineering

Model training and evaluation

Model deployment

Model monitoring and management

Model iteration and improvement

Case studies and real-world examples

Customer churn prediction

Fraud detection

Future trends in Spark ML and distributed ML

Summary

Part 5: Mock Papers

9

Mock Test 1

Questions

Answers

10

Mock Test 2

Questions

Answers

Index

Other Books You May Enjoy

Preface

Welcome to the comprehensive guide for aspiring developers seeking certification in Apache Spark with Python through Databricks.

In this book, Databricks Certified Associate Developer for Apache Spark Using Python, I have distilled years of expertise and practical wisdom into a comprehensive guide to navigate the complexities of data science, AI, and cloud technologies and help you prepare for Spark certification. Through insightful anecdotes, actionable insights, and proven strategies, I will equip you with the tools and knowledge needed to thrive in an ever-evolving technological landscape of big data and artificial intelligence.

Apache Spark has emerged as the go-to framework to process large-scale data, enabling organizations to extract valuable insights and drive informed decision-making. With its robust capabilities and versatility, Spark has become a cornerstone in the toolkit of data engineers, analysts, and scientists worldwide. This book is designed to be your comprehensive companion on the journey to mastering Apache Spark with Python, providing a structured approach to understanding the core concepts, advanced techniques, and best practices for leveraging Spark’s full potential.

This book is meticulously crafted to guide you on the journey to becoming a certified Apache Spark developer. With a focus on certification preparation, I offer a structured approach to mastering Apache Spark with Python, ensuring that you’re well-equipped to ace the certification exam and validate your expertise.

Who this book is for

This book is tailored for individuals aspiring to become certified developers in Apache Spark using Python. Whether you’re a seasoned data professional looking to validate your expertise or a newcomer eager to delve into the world of big data analytics, this guide caters to all skill levels. From beginners seeking a solid foundation in Spark to experienced practitioners aiming to fine-tune their skills and prepare for certification, this book serves as a valuable resource for anyone passionate about harnessing the power of Apache Spark.

Whether you’re aiming to enhance your career prospects, validate your skills, or secure new opportunities in the data engineering landscape, this guide is tailored to meet your certification goals. With a focus on exam preparation, we provide targeted resources and practical insights to ensure your success in the certification journey.

The book provides prescriptive guidance and associated methodologies to make your mark in big data space with working knowledge of Spark and help you pass your Spark certification exam. This book expects you to have a working knowledge of Python, but it does not expect any prior Spark knowledge, although having a working knowledge of PySpark would be very beneficial.

What this book covers

In the following chapters, we will cover the following topics.

Chapter 1

, Overview of the Certification Guide and Exam, introduces the basics of the certification exam in PySpark and how to prepare for it.

Chapter 2

, Understanding Apache Spark and Its Applications, delves into the fundamentals of Apache Spark, exploring its core functionalities, ecosystem, and real-world applications. It introduces Spark’s versatility in handling diverse data processing tasks, such as batch processing, real-time analytics, machine learning, and graph processing. Practical examples illustrate how Spark is utilized across industries and its evolving role in modern data architectures.

Chapter 3

, Spark Architecture and Transformations, deep-dives into the architecture of Apache Spark, elucidating the RDD (Resilient Distributed Dataset) abstraction, Spark’s execution model, and the significance of transformations and actions. It explores the concepts of narrow and wide transformations, their impact on performance, and how Spark’s execution plan optimizes distributed computations. Practical examples elucidate these concepts for better comprehension.

Chapter 4

, Spark DataFrames and their Operations, focuses on Spark’s DataFrame API and explores its role in structured data processing and analytics. It covers DataFrame creation, manipulation, and various operations, such as filtering, aggregations, joins, and groupings. Illustrative examples demonstrate the ease of use and advantages of the DataFrame API in handling structured data.

Chapter 5

, Advanced Operations and Optimizations in Spark and Optimization, expands on your foundational knowledge and delves into advanced Spark operations, including broadcast variables, accumulators, custom partitioning, and working with external libraries. It explores techniques to handle complex data types, optimize memory usage, and leverage Spark’s extensibility for advanced data processing tasks.

This chapter also delves into performance optimization strategies in Spark, emphasizing the significance of adaptive query execution. It explores techniques for optimizing Spark jobs dynamically, including runtime query planning, adaptive joins, and data skew handling. Practical tips and best practices are provided to fine-tune Spark jobs for enhanced performance.

Chapter 6

, SQL Queries in Spark, focuses on Spark’s SQL module and explores the SQL-like querying capabilities within Spark. It covers the DataFrame API’s interoperability with SQL, enabling users to run SQL queries on distributed datasets. Examples showcase how to express complex data manipulations and analytics using SQL queries in Spark.

Chapter 7

, Structured Streaming in Spark, focuses on real-time data processing and introduces Structured Streaming, Spark’s API for handling continuous data streams. It covers concepts such as event time processing, watermarking, triggers, and output modes. Practical examples demonstrate how to build and deploy streaming applications using Structured Streaming.

This chapter is not included in the Spark certification exam, but it is beneficial to understand streaming concepts, since they are a core concept in the modern data engineering world.

Chapter 8

, Machine Learning with Spark ML, explores Spark’s machine learning library, Spark ML, diving into supervised and unsupervised machine learning techniques. It covers model building, evaluation, and hyperparameter tuning for various algorithms. Practical examples illustrate the application of Spark ML in real-world machine learning tasks.

This chapter is not included in the Spark certification exam, but it is beneficial to understand machine learning concepts in Spark, since they are a core concept in the modern data science world.

Chapter 9

, Mock Test 1, provides you with the first mock test to prepare for the actual certification exam.

Chapter 10

, Mock Test 2, provides you with the second mock test to prepare for the actual certification exam.

To get the most out of this book

Before diving into the chapters, it’s essential to have a basic understanding of Python programming and familiarity with fundamental data processing concepts. Additionally, a grasp of distributed computing principles and experience with data manipulation and analysis will be beneficial. Throughout the book, we’ll assume a working knowledge of Python and foundational concepts in data engineering and analytics. With these prerequisites in place, you’ll be well-equipped to embark on your journey to becoming a certified Apache Spark developer.

The code will work best if you sign up for the community edition of Databricks and import the python files into your account.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Databricks-Certified-Associate-Developer-for-Apache-Spark-Using-Python

. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/

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Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: The createOrReplaceTempView() method allows us to save the processed data as a view in Spark SQL.

A block of code is set as follows:

# Perform an aggregation to calculate the average salary

average_salary = spark.sql(SELECT AVG(Salary) AS average_salary FROM employees)

Bold: Indicates a new term, an important word, or words that you see on screen. For instance, words in menus or dialog boxes appear in bold. Here is an example: The exam consists of 60 questions. The time you’re given to attempt these questions is 120 minutes.

Tips or important notes

Appear like this.

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