OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course Discontinued We've found some alternatives for you:

Top Active Alternatives

Processing Streaming Data Using Apache Spark Structured Streaming is no longer accepting new enrollments. We've matched the highest-rated active courses in this subject:

Read Reviews → Enroll Active
Read Reviews → Enroll Active
Read Reviews → Enroll Active
Pluralsight logo

Processing Streaming Data Using Apache Spark Structured Streaming

Janani Ravi

Structured streaming is the scalable and fault-tolerant stream processing engine in Apache Spark 2 which can be used to process high-velocity streams.

Stream processing applications work with continuously updated data and react to changes in real-time. In this course, Processing Streaming Data Using Apache Spark Structured Streaming, you'll focus on integrating your streaming application with the Apache Kafka reliable messaging service to work with real-world data such as Twitter streams.

Read more

Structured streaming is the scalable and fault-tolerant stream processing engine in Apache Spark 2 which can be used to process high-velocity streams.

Stream processing applications work with continuously updated data and react to changes in real-time. In this course, Processing Streaming Data Using Apache Spark Structured Streaming, you'll focus on integrating your streaming application with the Apache Kafka reliable messaging service to work with real-world data such as Twitter streams.

First, you’ll explore Spark’s architecture to support distributed processing at scale. Next, you will install and work with the Apache Kafka reliable messaging service.

Finally, you'll perform a number of transformation operations on Twitter streams, including windowing and join operations.

When you're finished with this course you will have the skills and knowledge to work with high volume and velocity data using Spark and integrate with Apache Kafka to process streaming data.

This course is no longer available. Find something similar by browsing:
Apache Spark Structured Streaming Apache Kafka Stream Processing Twitter

What's inside

Syllabus

Course Overview
Getting Started with the Spark Standalone Cluster
Integrating Spark with Apache Kafka
Performing Windowing Operations on Streams
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches about integrating streaming applications with Apache Kafka, a renowned reliable messaging service
Focuses on real-world data such as Twitter streams, providing practical applications
Covers key concepts like windowing and join operations, strengthening data processing capabilities
Begins with an overview of Spark's architecture, ensuring a solid foundation for understanding distributed processing
Builds a strong foundation for working with high-volume and high-velocity data using Spark
Taught by Janani Ravi, who is recognized for their expertise in data processing and stream processing

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Practical spark streaming with kafka integration

According to learners, this course offers a highly practical and effective introduction to processing streaming data using Apache Spark Structured Streaming, with a strong emphasis on integration with Apache Kafka. Students frequently highlight the clear explanations and valuable hands-on labs, particularly those involving real-world Twitter data streams. While providing a solid foundational understanding of Spark's stream processing capabilities, some suggest that prior familiarity with Spark or Scala/Python can be beneficial. Recent reviews indicate ongoing content improvements by the instructor, addressing earlier feedback and ensuring the material remains current and relevant for professionals.
Course content updated based on feedback and technology changes.
"It's great to see that the course content has been updated to reflect newer Spark versions, very helpful."
"The instructor seems very responsive to questions and continuously improves the material based on past feedback."
"I noticed improvements from earlier versions, which shows a strong commitment to quality and relevance."
Complex concepts are well-explained, supported by practical labs.
"The instructor breaks down complex topics into easily digestible parts, making Spark less intimidating."
"I found the practical labs to be highly effective in reinforcing the theoretical knowledge gained."
"The step-by-step guidance through coding examples really solidified my understanding of windowing operations."
Focuses on real-world scenarios and integration with Kafka.
"The integration of Spark Structured Streaming with Kafka was exactly what I needed for my project."
"The hands-on labs using Twitter data were really effective, directly applicable to my work."
"This course taught me how to effectively connect Spark Structured Streaming with Kafka topics and process data."
Covers core concepts effectively but advanced topics need further study.
"It does an excellent job with the fundamentals, but I'd like to see more on performance tuning for large scale jobs."
"The course is a great start, but I found I needed supplemental material for optimizing complex streaming applications."
"I wish there was more depth on error handling and advanced fault tolerance patterns within Spark Structured Streaming."
Provides a strong foundation, best with some prior Spark knowledge.
"While comprehensive, I feel some prior familiarity with Spark or Scala/Python is beneficial to keep up with the pace."
"This course provides a solid entry point, but it's not for absolute beginners in distributed computing concepts."
"I recommend brushing up on basic Spark concepts before diving into structured streaming here for a smoother experience."

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 Processing Streaming Data Using Apache Spark Structured Streaming with these activities:
Review Distributed Processing Concepts
Strengthen your understanding of distributed processing concepts to enhance your comprehension of Spark's architecture.
Show steps
  • Review textbooks or online resources on distributed systems
  • Complete practice exercises or quizzes to test your understanding
Organize and Review Course Materials
Set a solid foundation for learning by getting organized and reviewing key concepts.
Show steps
  • Gather all course materials, including notes, assignments, and resources
  • Organize materials into a logical structure
  • Review materials regularly to reinforce understanding
Explore Apache Kafka Tutorial
Gain familiarity with Apache Kafka, a key tool for working with streaming data in this course.
Browse courses on Apache Kafka
Show steps
  • Visit the official Apache Kafka website
  • Review the 'Getting Started' guide
  • Complete a beginner tutorial on Apache Kafka
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Data Transformation Operations
Solidify your understanding of data transformation operations commonly used in stream processing.
Browse courses on Data Transformation
Show steps
  • Use online resources or textbooks to review windowing and join operations
  • Practice these operations on sample datasets
  • Complete coding exercises or quizzes
Connect with Experienced Data Engineers
Gain insights and guidance by connecting with experienced professionals in the field of data engineering.
Show steps
  • Identify potential mentors through online platforms or networking events
  • Reach out to mentors, introduce yourself, and express your interest
  • Schedule regular meetings or discussions to seek advice and guidance
Attend a Spark Structured Streaming Workshop
Gain hands-on experience and learn advanced techniques by attending a workshop focused on Spark Structured Streaming.
Browse courses on Spark Structured Streaming
Show steps
  • Research and identify relevant workshops
  • Register and attend the workshop
  • Actively participate and ask questions
Build a Simple Stream Processing Application using Spark and Kafka
Apply your skills by building a functional stream processing application that integrates Apache Kafka and Spark.
Browse courses on Spark
Show steps
  • Design the application's architecture and data flow
  • Implement data ingestion using Apache Kafka
  • Implement data processing using Spark Structured Streaming
  • Deploy and test the application
Contribute to Open-Source Projects Related to Stream Processing
Dive deeper into stream processing by contributing to open-source projects and collaborating with the community.
Show steps
  • Identify open-source projects related to Spark Structured Streaming or stream processing
  • Review the project's documentation and guidelines
  • Contribute code, documentation, or bug fixes
  • Engage with the project's community through forums or discussions

Career center

Learners who complete Processing Streaming Data Using Apache Spark Structured Streaming will develop knowledge and skills that may be useful to these careers:
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software systems. This course can help build a foundation for becoming a Software Engineer by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for developing software systems that can handle high-volume and velocity data.
See salaries and explore the career path for Software Engineer
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data pipelines. This course can help build a foundation for becoming a Data Engineer by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for developing data pipelines that can handle high-volume and velocity data.
See salaries and explore the career path for Data Engineer
Data Scientist
A Data Scientist is responsible for analyzing data to extract insights and build models. This course can help build a foundation for becoming a Data Scientist by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for developing data science models that can handle high-volume and velocity data.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
A Machine Learning Engineer is responsible for building and deploying machine learning models. This course can help build a foundation for becoming a Machine Learning Engineer by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for developing machine learning models that can handle high-volume and velocity data.
See salaries and explore the career path for Machine Learning Engineer
Big Data Engineer
A Big Data Engineer is responsible for designing, building, and maintaining big data systems. This course can help build a foundation for becoming a Big Data Engineer by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for developing big data systems that can handle high-volume and velocity data.
See salaries and explore the career path for Big Data Engineer
Cloud Engineer
A Cloud Engineer is responsible for designing, building, and maintaining cloud computing systems. This course can help build a foundation for becoming a Cloud Engineer by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for developing cloud computing systems that can handle high-volume and velocity data.
See salaries and explore the career path for Cloud Engineer
Business Analyst
A Business Analyst is responsible for analyzing business problems and developing solutions. This course can help build a foundation for becoming a Business Analyst by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills can be used to analyze business problems and develop solutions that can handle high-volume and velocity data.
See salaries and explore the career path for Business Analyst
Data Analyst
A Data Analyst is responsible for analyzing data to extract insights. This course can help build a foundation for becoming a Data Analyst by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for analyzing data and extracting insights from high-volume and velocity data.
See salaries and explore the career path for Data Analyst
Software Architect
A Software Architect is responsible for designing and developing software systems. This course can help build a foundation for becoming a Software Architect by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for designing and developing software systems that can handle high-volume and velocity data.
See salaries and explore the career path for Software Architect
Systems Engineer
A Systems Engineer is responsible for designing, building, and maintaining systems. This course can help build a foundation for becoming a Systems Engineer by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for designing, building, and maintaining systems that can handle high-volume and velocity data.
See salaries and explore the career path for Systems Engineer
Data Warehouse Engineer
A Data Warehouse Engineer is responsible for designing, building, and maintaining data warehouses. This course can help build a foundation for becoming a Data Warehouse Engineer by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills are essential for designing, building, and maintaining data warehouses that can handle high-volume and velocity data.
See salaries and explore the career path for Data Warehouse Engineer
DevOps Engineer
A DevOps Engineer is responsible for bridging the gap between development and operations. This course can help build a foundation for becoming a DevOps Engineer by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills can be used to develop and operate systems that can handle high-volume and velocity data.
See salaries and explore the career path for DevOps Engineer
Database Administrator
A Database Administrator is responsible for managing and maintaining databases. This course can help build a foundation for becoming a Database Administrator by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills can be used to manage and maintain databases that can handle high-volume and velocity data.
See salaries and explore the career path for Database Administrator
Information Architect
An Information Architect is responsible for designing and organizing information systems. This course can help build a foundation for becoming an Information Architect by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills can be used to design and organize information systems that can handle high-volume and velocity data.
See salaries and explore the career path for Information Architect
Information Security Analyst
An Information Security Analyst is responsible for protecting information systems from threats. This course can help build a foundation for becoming an Information Security Analyst by providing skills in processing streaming data using Apache Spark Structured Streaming. This course covers the architecture of Spark, how to integrate Spark with Apache Kafka, and how to perform windowing and join operations on streams. These skills can be used to protect information systems from threats that can exploit high-volume and velocity data.
See salaries and explore the career path for Information Security Analyst

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Processing Streaming Data Using Apache Spark Structured Streaming.
Cover image
Learning Spark
Save
Provides a comprehensive introduction to Apache Spark, covering both the core concepts and advanced topics. It valuable resource for anyone who wants to learn more about Spark and use it to build real-world applications.
Learning Spark: Lightning-Fast Big Data Analysis
Paperback
$$
Cover image
Machine Learning with Spark
Save
Covers the fundamentals and advanced concepts of Apache Spark's structured streaming engine. It provides hands-on examples and best practices for building scalable and fault-tolerant streaming applications.
Machine Learning with Spark - Second Edition:...
Paperback
$$$
Machine Learning with Spark - Second Edition
Kindle Edition
$$
Cover image
Advanced Analytics with PySpark
Save
Provides a practical guide to using Apache Spark with Python. It covers a wide range of topics, including data ingestion, transformation, and analysis.
Advanced Analytics with PySpark: Patterns for...
Paperback
$$
Advanced Analytics with PySpark
Kindle Edition
$$
Cover image
Kafka: The Definitive Guide
Save
Is the authoritative guide to Apache Kafka, covering its design, deployment, and operation. It provides deep insights into the internals of Kafka and valuable reference for anyone working with this technology.
Kafka: The Definitive Guide: Real-Time Data and...
Paperback
$$
Cover image
Data Science for Business
Save
Provides a practical introduction to data science, with a focus on business applications. It good resource for anyone who wants to learn how to use data to make better decisions.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
Drawing Codes
Save
Provides a gentle introduction to machine learning, with a focus on making it accessible to beginners. It good resource for anyone who wants to learn the basics of machine learning.
Drawing Codes: Experimental Protocols of...
Hardcover
$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning, including its history, foundations, and applications. It good resource for anyone who wants to learn more about deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Reinforcement Learning, second edition
Save
Provides a comprehensive overview of reinforcement learning, including its history, foundations, and applications. It good resource for anyone who wants to learn more about reinforcement learning.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Natural Language Processing with Python
Save
Provides a comprehensive overview of natural language processing, including its history, foundations, and applications. It good resource for anyone who wants to learn more about NLP.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive overview of statistical learning, including its history, foundations, and applications. It good resource for anyone who wants to learn more about statistical learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Information Theory, Inference and Learning...
Save
Provides a comprehensive overview of information theory, inference, and learning algorithms. It good resource for anyone who wants to learn more about these topics.
Information Theory, Inference and Learning...
Hardcover
$$$
Cover image
Pattern Recognition and Machine Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning, including its history, foundations, and applications. It good resource for anyone who wants to learn more about these topics.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Processing Streaming Data Using Apache Spark Structured Streaming.
Kafka Integration with Storm, Spark, Flume, and Security
Most relevant
Cloud Computing Applications, Part 2: Big Data and...
Most relevant
Apache Kafka - An Introduction
Most relevant
Apache Spark 3 - Real-time Stream Processing using Scala
Most relevant
Apache Flink with Scala 3
Most relevant
Data Engineering using Kafka and Spark Structured...
Most relevant
30 Days of Python | Unlock your Python Potential
Most relevant
Beginning Data Exploration and Analysis with Apache Spark
Most relevant
Developing Spark Applications Using Scala & Cloudera
Most relevant

Pluralsight Buyer Protection & Guarantee

Verified partner policies & purchase protections

Enterprise Technology Platform
Free Trial & Audit

10-Day Free Trial

10-day free trial (up to 200 minutes of content) with access to the Skills video library and skill assessments. Cancel before day 10 to avoid automatic conversion.

Return & Refund Policy

Cancel Anytime (Non-Refundable Once Billed)

Cancel easily online anytime before the 10-day trial expires to prevent billing. Once subscription fees are paid, individual subscriptions are non-refundable (cancellation stops future renewals).

Buyer Protections

Verified Academic & Industry Partner

Skill IQ and Role IQ diagnostic benchmark assessments, hands-on lab environments, and curated learning paths designed by technology leaders.

Key Purchase Protections
10-day free trial on individual Skills plans
Skill IQ assessments to benchmark proficiency
Cancel anytime online before trial ends without fees
Hands-on interactive labs and enterprise-grade tech content
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Time
155 hours
Level
Intermediate
Via
Pluralsight
Instructor
Janani Ravi
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches about integrating streaming applications with Apache Kafka, a renowned reliable messaging service
Focuses on real-world data such as Twitter streams, providing practical applications
Covers key concepts like windowing and join operations, strengthening data processing capabilities
Begins with an overview of Spark's architecture, ensuring a solid foundation for understanding distributed processing
Builds a strong foundation for working with high-volume and high-velocity data using Spark
Taught by Janani Ravi, who is recognized for their expertise in data processing and stream processing
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
This course is currently discontinued or unavailable. See active alternatives above.
Course Unavailable
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser