Master the tools and techniques that power large-scale data processing and analytics. This course introduces the principles and frameworks of Big Data Processing with Hadoop and Spark, enabling learners to manage, process, and analyze massive datasets efficiently.
Big Data Processing with Hadoop and Spark
Big Data Processing with Hadoop and Spark
This course is part of Cloud Computing for Data Science Specialization
Instructor: Dmitriy Babichenko
Included with
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
9 hours to complete
Flexible schedule
Learn at your own pace
Build toward a degree
What you'll learn
Explain how Hadoop and Spark enable large-scale data processing.
Build and manage distributed data pipelines using Hadoop frameworks.
Implement in-memory analytics and real-time processing with Spark.
Apply big data tools to design scalable, data-driven applications.
Skills you'll gain
- PySpark
- Data Pipelines
- Apache Hadoop
- Data Analysis
- Data Transformation
- Big Data
- Distributed Computing
- Data Science
- Data Management
- Data Processing
- Apache Hive
- Data Storage
- Apache Spark
- Scikit Learn (Machine Learning Library)
- Information Technology
- Scalability
- Data Storage Technologies
- Predictive Modeling
- Python Programming
- Skills section collapsed. Showing 13 of 19 skills.
Details to know
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Recently updated!
February 2026
Assessments
8 assignments
Taught in English
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This course is part of the Cloud Computing for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
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There are 3 modules in this course
This module guides you through the core components of the Hadoop ecosystem, starting with its architecture and distributed file system. You’ll explore how Hadoop processes data, gain insight into its broader ecosystem, and apply your knowledge in hands-on activities using both Docker and a Linux virtual machine.
What's included
6 videos1 reading3 assignments
This module introduces you to key programming models for distributed data processing, with a focus on MapReduce and its practical applications. You'll explore core concepts and terminology, work through guided code walkthroughs using Python to implement word count and server log analysis tasks, and gain experience using Apache Pig for data transformation. You'll also gain hands-on experience writing data transformation scripts in Apache Pig, culminating in an assignment that applies these skills to web log analysis.
What's included
6 videos6 readings3 assignments
This module introduces you to Apache Spark, covering its core concepts, architecture, and machine learning capabilities through MLlib. You’ll learn how to set up Spark using Docker and Linux VM, explore how PySpark operates within the Spark framework, and compare Spark MLlib with scikit-learn through hands-on code walkthroughs. By the end of the module, you'll apply what you've learned in graded activities and an assignment focused on building a predictive model with PySpark and MLlib.
What's included
5 videos3 readings2 assignments
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Build toward a degree
This course is part of the following degree program(s) offered by University of Pittsburgh. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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