Apache Spark for Data Engineering and Machine Learning from edX

Apache® Spark™ is a fast, flexible, and developer-friendly open-source platform for large-scale SQL, batch processing, stream processing, and machine learning. Users can take advantage of its open-source ecosystem, speed, ease of use, and analytic capabilities to work with Big Data in new ways.

In this short course, you explore concepts and gain hands-on skills to use Spark for data engineering and machine learning applications. You'll learn about Spark Structured Streaming, including data sources, output modes, operations. Then, explore how Graph theory works and discover how GraphFrames supports Spark DataFrames and popular algorithms.

Organizations can acquire data from structured and unstructured sources and deliver the data to users in formats they can use. Learn how to use Spark for extract, transform and load (ETL) data. Then, you'll hone your newly acquired skills during your "ETL for Machine Learning Pipelines" lab.

Next, discover why machine learning practitioners prefer Spark. You'll learn how to create pipelines and quickly implement features for extraction, selections, and transformations on structured data sets. Discover how to perform classification and regression using Spark. You'll be able to define and identify both supervised and unsupervised learning. Learn about clustering and how to apply the k-mean s clustering algorithm using Spark MLlib. You'll reinforce your knowledge with focused, hands-on labs and a final project where you will apply Spark to a real-world inspired problem.

Prior to taking this course, please ensure you have foundational Spark knowledge and skills, for example, by first completing the IBM course titled "Big Data, Hadoop and Spark Basics."

What's inside

Learning objectives

Differentiate between supervised and unsupervised machine learning"
Describe the features, benefits, limitations, and application of apache spark structured streaming
Describe graph theory and explain how graphframes benefits developers
Explain how developers can apply extract, transform and load (etl) processes using spark.

Describe how spark ml supports machine learning development
Apply spark ml for regression and classification
Explain how spark ml uses clustering
Demonstrate hands-on working knowledge of using spark for etl processes

Differentiate between supervised and unsupervised machine learning"
Describe the features, benefits, limitations, and application of apache spark structured streaming
Describe graph theory and explain how graphframes benefits developers
Explain how developers can apply extract, transform and load (etl) processes using spark.
Describe how spark ml supports machine learning development
Apply spark ml for regression and classification
Explain how spark ml uses clustering
Demonstrate hands-on working knowledge of using spark for etl processes

Syllabus

Module 1 – Spark for Data Engineering

Spark Structured Streaming

GraphFrames on Apache Spark

ETL Workloads

Hands-on Lab: ETL for ML Pipelines

Module 2 – Spark ML for Machine Learning

Spark ML Fundamentals

Spark ML Regression and Classification

Spark ML Clustering

Module 3 – Final Project

o Lab: Setup & Practice Assignment

o Project Overview

o Lab: Final Assignment Project

o Project Submission & Grading

Final Quiz

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Helps students quickly ramp up their skills with Apache Spark, which is highly relevant to the data field

Well aligned for students with existing Apache Spark knowledge and a foundational understanding of Hadoop

Provides hands-on labs and projects, allowing students to apply their learnings in a practical setting

Instructors are recognized experts in the Apache Spark field

May require students to take additional foundational courses, such as Big Data, Hadoop and Spark Basics, before enrolling

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Apache Spark for Data Engineering and Machine Learning with these activities:

Review Python and SQL

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Review the fundamentals of Python and SQL to reinforce foundational knowledge and ensure a strong base for the course

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Review Python syntax and data structures
Review SQL queries and database management

Compile Resources on Supervised vs Unsupervised Learning

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Gather relevant articles, tutorials, and videos that explain the key concepts and differences between supervised and unsupervised learning, providing a comprehensive understanding of the topic

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Organize and categorize the materials
Search for online resources

Practice Spark Structured Streaming

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Complete hands-on exercises to reinforce concepts and gain practical experience with Spark Structured Streaming

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Create data sources and output modes
Apply operations to streams

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Explore Graph Theory with GraphFrames

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Follow online tutorials or documentation to gain hands-on experience with GraphFrames, enhancing understanding of how graph theory concepts can be applied in Apache Spark

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Install GraphFrames library
Create and manipulate graphs
Apply graph algorithms

Develop an ETL Pipeline with Spark

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Build an ETL pipeline that leverages Spark's capabilities to extract, transform, and load data, solidifying understanding of data engineering techniques

Browse courses on ETL Processes

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Design the data ingestion process
Implement data transformations
Configure data loading

Train a Machine Learning Model with Spark ML

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Develop a machine learning model using Spark ML, applying classification and regression techniques to gain practical experience in data analysis

Browse courses on Machine Learning

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Prepare the training data
Build and train a model
Evaluate model performance

Become a Peer Mentor for Spark Concepts

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Provide guidance and support to peers who are new to Spark, fostering a collaborative learning environment and reinforcing your own understanding of the concepts

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Connect with students through online forums or study groups
Share knowledge and provide assistance
Reflect on your own understanding

Contribute to an Apache Spark Project

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Dive deeper into Spark by contributing to open-source projects, experiencing real-world applications and expanding your understanding of the platform's capabilities

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Identify a project to contribute to
Review the project's documentation
Make code modifications and submit pull requests

Career center

Learners who complete Apache Spark for Data Engineering and Machine Learning will develop knowledge and skills that may be useful to these careers:

Data Engineer

Data Engineers have a firm grasp of data and its uses. A course designed to teach Spark, a tool for large-scale data processing, can greatly aid someone looking to become a Data Engineer. Its modules on topics such as ETL and GraphFrames are indispensable for the field. As an expert career advisor, I would highly recommend this course if you seek a career as a Data Engineer.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine Learning Engineers design, develop and maintain Machine Learning systems. Since Apache Spark is a popular framework for Machine Learning, this course on Apache Spark will help budding Machine Learning Engineers build an essential skillset. Modules in this course that focus on clustering and other elements of Spark ML are of paramount importance. If you're thinking of a career as a Machine Learning Engineer, I'd strongly suggest you consider taking this course.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts regularly work with structured and unstructured data sets acquired from heterogeneous sources. This course on Apache Spark for Data Engineering and Machine Learning is an excellent resource for beginners or intermediate-level Data Analysts. The course's focus on Spark ETL and Spark ML will prove to be quite valuable in transforming data into actionable insights and building predictive models. If you seek a career as a Data Analyst, you should definitely consider this course.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Science combines Machine Learning, statistics, and data analysis. This course is a great fit for someone who wants to be a Data Scientist because it teaches you how to use Apache Spark for large-scale data processing and Machine Learning. Modules on topics like Spark ML, regression, and classification are imperative for a successful career as a Data Scientist.

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Software Engineer

Software Engineers create and maintain software systems. This course can be valuable to someone interested in becoming a Software Engineer since Apache Spark is widely used for Big Data processing. The course's focus on Spark Structured Streaming and GraphFrames on Apache Spark are highly relevant in this field. If you dream of becoming a Software Engineer, think about taking this course.

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Database Administrator

Database Administrators take care of the installation, configuration, and maintenance of databases. This course in Apache Spark for Data Engineering and Machine Learning may be somewhat helpful to someone looking to become a Database Administrator. Modules such as hands-on labs on ETL for ML Pipelines may be of some utility in this field.

See salaries and explore the career path for Database Administrator

Business Analyst

Business Analysts study data and processes within a company to identify inefficiencies and improvement opportunities. While this course does not directly map to a Business Analyst's daily work, it may be of some tangential benefit. Modules on ETL Workloads might be of interest to someone curious about the data engineering side of a business. However, I would not prioritize this course if your goal is to become a Business Analyst.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians collect, analyze, and interpret data to provide meaningful insights. This course on Apache Spark for Data Engineering and Machine Learning may be of some benefit to someone looking to become a Statistician. In particular, the modules on Spark ML fundamentals, regression, and classification may be of interest. That said, there are courses more directly relevant to Statistics.

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Systems Analyst

Systems Analysts study the current systems and procedures within a company to identify opportunities for improvement. This course on Apache Spark for Data Engineering and Machine Learning may be somewhat useful to aspiring Systems Analysts. Modules on topics such as ETL Workloads might be of some help.

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Web Developer

Web Developers design and develop websites. While this course is not directly relevant to Web Development, some modules may provide tangential benefits. For example, the lab on ETL for ML Pipelines may be useful for someone hoping to learn how to manage a lot of data in the context of a website. However, I would generally not recommend this course for those seeking a Web Developer role.

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Computer Programmer

Computer Programmers write and test code to create software applications. This course may be somewhat helpful to aspiring Computer Programmers, as Apache Spark is a popular framework for Big Data processing. Modules like Spark Structured Streaming and GraphFrames for Apache Spark could be of some assistance.

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Computer Scientist

Computer Scientists study computation and information. This course on Apache Spark for Data Engineering and Machine Learning may be somewhat useful due to its focus on Big Data processing. Modules on Spark ML, including clustering, may also be of interest to aspiring Computer Scientists. However, I would generally recommend more foundational courses in this field.

See salaries and explore the career path for Computer Scientist

Financial Analyst

Financial Analysts collect and analyze financial data to make recommendations about investments. This course on Apache Spark for Data Engineering and Machine Learning may be of some tangential benefit to someone looking to become a Financial Analyst. The module on ETL Workloads may be of interest. However, it is generally not the right choice for those seeking a Financial Analyst role.

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Operations Research Analyst

Operations Research Analysts use advanced analytical techniques to improve efficiency and effectiveness in a variety of industries. While this course is not directly relevant to Operations Research, the module on GraphFrames on Apache Spark could be of some interest. However, there are other courses that would be a more direct fit for this career.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk. This course on Apache Spark for Data Engineering and Machine Learning may be of some tangential benefit to those seeking an Actuary role. However, the course's focus on Big Data processing is not directly relevant to the day-to-day tasks of an Actuary. There are other courses that would be a more direct fit for this career.

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Apache Spark for Data Engineering and Machine Learning

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