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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.
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.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/h50wty/processing
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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
Syllabus
Course Overview
Getting Started with the Spark Standalone Cluster
Integrating Spark with Apache Kafka
Performing Windowing Operations on Streams
Read more
Performing Join Operations on Streams
Good to know
Good to know
Know what's good ,
what to watch for , 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
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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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review Distributed Processing Concepts
Show steps
Strengthen your understanding of distributed processing concepts to enhance your comprehension of Spark's architecture.
Show steps
Show steps
Show steps
- Review textbooks or online resources on distributed systems
- Complete practice exercises or quizzes to test your understanding
Organize and Review Course Materials
Show steps
Set a solid foundation for learning by getting organized and reviewing key concepts.
Show steps
Show steps
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
Show steps
Gain familiarity with Apache Kafka, a key tool for working with streaming data in this course.
Browse courses on
Apache Kafka
Show steps
Show steps
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
Show steps
Solidify your understanding of data transformation operations commonly used in stream processing.
Browse courses on
Data Transformation
Show steps
Show steps
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
Show steps
Gain insights and guidance by connecting with experienced professionals in the field of data engineering.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Apply your skills by building a functional stream processing application that integrates Apache Kafka and Spark.
Browse courses on
Spark
Show steps
Show steps
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
Show steps
Dive deeper into stream processing by contributing to open-source projects and collaborating with the community.
Show steps
Show steps
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:
Big Data 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.
Machine Learning Engineer
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.
Software Engineer
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.
Data 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.
Cloud 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.
Data Scientist
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.
Information Security Analyst
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.
Systems Engineer
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.
Data Warehouse 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.
DevOps 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.
Information Architect
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.
Business Analyst
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.
Software Architect
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.
Data 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.
Database Administrator
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.
For more career information including salaries, visit:
OpenCourser.com/course/h50wty/processing
Reading list
We've selected 14 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.
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.
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.
Provides a practical guide to using Apache Kafka Streams for real-time data processing. It covers a wide range of topics, including data ingestion, transformation, and analysis.
Provides a practical guide to using Apache Spark with Python. It covers a wide range of topics, including data ingestion, transformation, and analysis.
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.
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.
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.
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.
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.
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.
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.
Save
Provides a comprehensive overview of computer vision, including its history, foundations, and applications. It good resource for anyone who wants to learn more about computer vision.
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.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It good resource for anyone who wants to learn more about these topics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/h50wty/processing
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Time
155 hours
Level
Intermediate
Via
Pluralsight
Instructor
Janani Ravi
Language
English
Good to know
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
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