CF

Spark for Data Engineers

4h 28m 11s
English
Paid

Spark for Data Engineers is a 15-lesson 4 hours 28 minutes self-paced course by Joseph Machado. Master Apache Spark — one of the key tools for a data engineer.

Course facts

Lessons
15
Duration
4 hours 28 minutes
Level
All levels
Language
English
Updated
Instructor
Joseph Machado
Price
Premium

Master Apache Spark — one of the key tools for a data engineer. This course will help you confidently work with distributed data processing, optimize pipelines, and create high-performance architectures for Big Data projects. Minimum theory, maximum practice — all material is focused on real work tasks.

What Awaits You in This Course

You will journey from setting up the environment to developing a full end-to-end data processing pipeline. The course is structured as a practical workshop, with each topic reinforced through assignments and work with real datasets.

Course Program

1. Introduction, Goals, and Environment Setup

You will get acquainted with the course topics and the results you will achieve. Step by step, you will set up a local environment and infrastructure for practical assignments, allowing you to immediately start working with Spark clusters.

2. Data Processing in Apache Spark

A detailed study of Spark's internal architecture and key tools for distributed data processing:

  • Spark I/O: specifics of reading and writing data from various systems and formats.
  • DataFrame API: writing efficient analytical code based on Spark SQL.
  • Application Architecture: interaction between Driver and Executors, task distribution.
  • Transformations: differences between Narrow and Wide transformations and their impact on performance.
  • Query Plan: analysis of physical and logical query execution plans.
  • Spark UI: analysis of jobs, stages, and identifying performance bottlenecks.

3. Data Storage Patterns for Pipeline Optimization

You will learn how to properly organize data to speed up analytics and reduce cluster load:

  • Columnar Format: advantages of modern storage formats (Parquet, ORC).
  • Partitioning: optimizing folder structure based on frequent queries.
  • Bucketing: effective partitioning of high-cardinality columns.

4. Optimization of Processes in Apache Spark

Work techniques for speeding up Spark applications and improving their stability:

  • Code Optimization: practices for writing performant transformations.
  • Spark Configurations: parameters affecting resource allocation and task execution speed.

5. Final Capstone Project

At the final stage, you will assemble a complete data processing pipeline from loading to visualizing results:

  • Real Dataset: working with structured StackOverflow data approximately 50 GB in volume.
  • End-to-End Solution: goal setting, architecture design, processing, optimization, and creating final dashboards with KPIs.

Who Will Benefit From This Course

The course will be useful for data engineers, analysts, backend developers, and anyone who wants to delve into working with distributed computing and industrial data pipelines.

Learning Outcomes

  • Confident mastery of Apache Spark and the DataFrame API.
  • Skills in optimizing queries, pipelines, and data architectures.
  • Understanding of the internal mechanisms of distributed processing.
  • A finished project for your portfolio with a real Big Data dataset.

Who teaches Spark for Data Engineers? Joseph Machado

Joseph Machado thumbnail

After more than 15 years of developing distributed data platforms processing exabytes of information, I've spent countless hours sifting through official documentation, little-known blogs, and trial and error—all for the sake of simply grasping best practices and real technological trade-offs.

Most educational resources lacked depth. They were either optimized for SEO promotion or sponsored by specific vendors who only spoke about the advantages of their products.

I created the project Start Data Engineering to address this. My goal is to provide engineers with quality, project-based, and accessible content that truly prepares them for the tough realities of the working world. Over 20,000 professionals have already used these guides to enhance their skills.

Three main principles of my courses:

  • Practical applicability: You gain knowledge that can be applied in your work right away, rather than just reading and forgetting.
  • Code-first approach: All lessons are built on practice, forcing you to learn through real action.
  • Honest assessment of trade-offs: No blind faith in tools—I explain all the pros and cons so you clearly understand when and which technology to apply.

What lessons are included in Spark for Data Engineers?

This is a demo lesson (10:00 remaining)

You can watch up to 10 minutes for free. Subscribe to unlock all 15 lessons in this course and access 10,000+ hours of premium content across all courses.

View Pricing
0:00
/
#1: Course Objective
All Course Lessons (15)
#Lesson TitleDurationAccess
1
Course Objective Demo
05:50
2
Downloading the code & Setting up Infrastructure
03:10
3
Setting up data and how to use the notebooks for this course
05:39
4
[Spark IO] Spark can read data from and write data to most systems and formats
25:24
5
[Dataframe API] is the Pythonic equivalent of Spark SQL
22:17
6
[Spark application] is made up of one driver and one-or-more executor
08:42
7
[Distributed data transformations] are of two types Narrow & Wide
25:09
8
[Query plan] is how Spark plans to execute your logic
21:40
9
[Spark UI] to see statistics of how your data was processed
21:58
10
[Columnar format] is critical for large-scale data warehousing
23:05
11
[Partitioning] Splitting data into folders based on commonly filtered-column(s)
21:06
12
[Bucketing] is partitioning for high-cardinality columns
17:05
13
[Coding Techniques] for Optimal Data Processing in Apache Spark
15:51
14
[Spark Configurations] for optimal data processing
17:14
15
[End-to-end data pipeline] for 50GB Stackoverflow Data Analysis
34:01
Unlock unlimited learning

Get instant access to all 14 lessons in this course, plus thousands of other premium courses. One subscription, unlimited knowledge.

Learn more about subscription

What courses are similar to Spark for Data Engineers?

Frequently asked questions

What prerequisites should I have before enrolling in this course?
Before enrolling, you should have a foundational understanding of data engineering concepts and some experience with Python programming. Familiarity with distributed data processing and basic SQL queries will be beneficial as the course involves working with the DataFrame API and Spark SQL.
What will I build during this course?
Throughout the course, you will develop a full end-to-end data processing pipeline. This includes setting up the environment, working with Spark clusters, and analyzing 50GB of Stackoverflow data. The course is structured as a workshop, allowing you to apply what you learn directly to real datasets.
Who is the target audience for this course?
This course is designed for data engineers and professionals who want to deepen their understanding of Apache Spark and its applications in Big Data projects. It is also suitable for those looking to optimize data processing pipelines and develop high-performance data architectures.
How does the depth of this course compare to other courses on Apache Spark?
The course focuses on practical, real-world applications of Apache Spark with minimal theoretical content. It covers critical components such as Spark I/O, DataFrame API, and the Spark UI, providing a comprehensive understanding of Spark's internal architecture and performance optimization techniques.
What specific tools or platforms will I use in the course?
You will work extensively with Apache Spark, utilizing tools like the DataFrame API and Spark SQL. The course also covers Spark configurations and the use of the Spark UI for analyzing job performance and optimizing data processing.
What topics are not covered in this course?
The course does not cover non-Spark related data engineering tools or topics such as machine learning or data visualization. It is focused solely on Apache Spark and its application in data processing pipelines.
How much time should I expect to commit to this course?
The course consists of 15 lessons, each designed to provide hands-on experience with real datasets. While the total runtime is not specified, the practical nature of the course suggests that a significant time commitment will be needed to complete assignments and fully grasp the material.