CF

Fundamentals of Apache Spark and PySpark

2h 20m 54s
English
Paid

Fundamentals of Apache Spark and PySpark is a 29-lesson 2 hours 20 minutes self-paced course by Zero To Mastery. Apache Spark is an essential tool for any aspiring Data Engineer or Data Scientist, and PySpark allows you to harness the full power of Spark using the familiar Python programming language.

Course facts

Lessons
29
Duration
2 hours 20 minutes
Level
All levels
Language
English
Updated
Instructor
Zero To Mastery
Price
Premium

Apache Spark is an essential tool for any aspiring Data Engineer or Data Scientist, and PySpark allows you to harness the full power of Spark using the familiar Python programming language.

Course Overview

This comprehensive course is designed for individuals eager to confidently explore the world of big data. You will delve into Spark's architecture, learn to write clear and efficient PySpark code, and gain the skills to create scalable data processing pipelines.

Hands-On Learning Experience

Our training is practice-based, ensuring you work with real datasets, tackle practical tasks, and develop skills that are in high demand among employers.

Key Learning Objectives

  • Understand the architecture and components of Apache Spark.
  • Write efficient and maintainable PySpark code.
  • Create and manage scalable data processing pipelines.

Why Enroll in This Course?

If your goal is to learn how to analyze massive amounts of data, swiftly clean and transform information, and master the tools used by industry leaders like Netflix and Amazon, this course is the perfect fit for you.

Additional

  • https://github.com/mushketyk/ztm-data-engineering/tree/main/02-data-processing-with-spark
  • The exercises folder contains the starter code and solutions for the exercises in this course.
    • https://github.com/mushketyk/ztm-data-engineering/tree/main/02-data-processing-with-spark/exercises

Who teaches Fundamentals of Apache Spark and PySpark? Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

What lessons are included in Fundamentals of Apache Spark and PySpark?

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#1: Introduction
All Course Lessons (29)
#Lesson TitleDurationAccess
1
Introduction Demo
07:30
2
[Optional] What Is a Virtualenv?
06:37
3
Apache Spark
03:44
4
How Spark Works
04:24
5
Spark Application
07:41
6
DataFrames
06:43
7
Installing Spark
05:51
8
Inside Airbnb Data
07:02
9
Writing Your First Spark Job
07:05
10
Lazy Processing
02:16
11
[Exercise] Basic Functions
01:29
12
[Exercise] Basic Functions - Solution
06:41
13
Aggregating Data
04:00
14
Joining Data
04:40
15
Aggregations and Joins with Spark
06:10
16
Complex Data Types
05:09
17
[Exercise] Aggregate Functions
00:50
18
[Exercise] Aggregate Functions - Solution
05:54
19
User Defined Functions
03:25
20
Data Shuffle
06:14
21
Data Accumulators
03:42
22
Optimizing Spark Jobs
07:39
23
Submitting Spark Jobs
04:29
24
Other Spark APIs
05:16
25
Spark SQL
04:33
26
[Exercise] Advanced Spark
02:10
27
[Exercise] Advanced Spark - Solution
05:26
28
Summary
03:08
29
Let's Keep Learning Together!
01:06
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Frequently asked questions

What are the prerequisites for enrolling in this course?
This course is designed for individuals with a basic understanding of Python programming. Familiarity with data analysis concepts is beneficial but not mandatory. An optional lesson on Virtualenv is provided for those unfamiliar with virtual environments, which can help manage Python dependencies when working with PySpark.
What kind of projects will I work on during the course?
The course includes hands-on exercises with real datasets, such as the Inside Airbnb data, to help you develop practical skills. You will write Spark jobs, perform data aggregation and joining, and work with complex data types. These projects aim to simulate real-world data processing tasks a data engineer or scientist might encounter.
Who is the target audience for this course?
This course is ideal for aspiring data engineers and data scientists who want to learn how to process and analyze large datasets using Apache Spark and PySpark. It is also suitable for professionals in related fields looking to expand their skill set to include big data technologies.
How does the depth of this course compare to other similar courses?
The course provides a comprehensive exploration of Apache Spark and PySpark, covering everything from basic DataFrame operations to advanced topics like data shuffling and optimizing Spark jobs. It combines theoretical understanding with practical application, offering a balanced learning experience compared to other courses that might focus more heavily on one aspect.
What specific tools or platforms will I learn to use?
You will learn to use Apache Spark and its components with the PySpark API, which enables Python-based big data processing. The course also covers Spark SQL for querying data, and additional Spark APIs for various data processing needs.
What topics are not covered in this course?
While the course covers a wide range of topics related to Apache Spark and PySpark, it does not delve into machine learning techniques or specific Spark-based machine learning libraries. The focus remains on data processing and pipeline creation.
How can the skills gained from this course be applied to other careers?
The skills learned in this course, such as writing efficient PySpark code and managing data processing pipelines, are valuable in any career involving big data analysis. These skills are applicable not only in data engineering and data science but also in roles within analytics and business intelligence where data processing is key.