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Szymon Warda
CPUs have more and more cores, but writing parallel programs is tricky. In this course, you will learn how the data flow programming model combined with the actor model makes writing high performance, large data-processing systems easy.
Read more
CPUs have more and more cores, but writing parallel programs is tricky. In this course, you will learn how the data flow programming model combined with the actor model makes writing high performance, large data-processing systems easy.
Read more
CPUs have more and more cores, but writing parallel programs is tricky. In this course, you will learn how the data flow programming model combined with the actor model makes writing high performance, large data-processing systems easy.
Writing a highly parallel application is tricky, but it doesn't have to be; with the proper tools it can be significantly simplified. In this course, Advanced Data and Stream Processing with Microsoft TPL Dataflow, you will learn how to take advantage of both the data flow programming model and the actor model implemented in Microsoft TPL Dataflow to write systems capable of quickly processing hundreds of gigabytes of data. First, you will explore the architectural principles of TPL Dataflow, including some of the pitfalls of abstraction over executed code-blocks. Next, you will use blocks to construct production-grade workflows with proper error handling and monitoring. Finally, you will learn how the imperative approach to execution logic makes parallelizing and performance optimization a breeze. Finishing this course will give you a unique tool to write systems that can handle large amounts of data, or even just high-performance systems that take advantage of all the processing power available on the machine without sacrificing code readability and reuse.
Register for this course and see more details by visiting:
OpenCourser.com/course/1ov4zk/advanced
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What's inside
Syllabus
Course Overview
Is TPL Dataflow Right for Your Problem?
TPL Dataflow Building Blocks
Building an Efficient Pipeline with Parallelization, Filtering, and Customization
Read more
Syllabus
Course Overview
Is TPL Dataflow Right for Your Problem?
TPL Dataflow Building Blocks
Building an Efficient Pipeline with Parallelization, Filtering, and Customization
Read more
Performance and Monitoring
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops an understanding of the actor model implemented in Microsoft TPL Dataflow
Teaches the data flow programming model with Microsoft TPL Dataflow
Suitable for learners who have experience with high performance systems, large data-processing systems, or Microsoft TPL Dataflow
Emphasizes performance and monitoring, which are essential concepts for large data-processing systems
May not be suitable for beginners in software development or data processing
Does not cover advanced topics such as deep learning or machine learning
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Save Advanced Data and Stream Processing with Microsoft TPL Dataflow to your list so you can find it easily later:
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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 Advanced Data and Stream Processing with Microsoft TPL Dataflow with these
activities:
Review of Basic Data Structures
Show steps
Review the basic data structures covered in a previous data structures course or complete a refresher course on data structures.
Browse courses on
Data Structures
Show steps
-
Review lecture notes or textbooks on data structures
-
Complete practice problems on basic data structures
Review data flow programming concepts
Show steps
Review key concepts in data flow programming to strengthen your foundational understanding before starting the course.
Browse courses on
Programming
Show steps
-
Identify key concepts in data flow programming, such as data streams, operators, and graphs.
-
Review different data flow programming models and their advantages and disadvantages.
-
Practice creating simple data flow programs to gain hands-on experience.
Build a data processing pipeline with TPL Dataflow
Show steps
Gain practical experience in using TPL Dataflow to build a data processing pipeline, reinforcing the concepts learned in the course.
Browse courses on
Parallel Programming
Show steps
-
Design a data processing pipeline for a specific data processing task.
-
Implement the pipeline using TPL Dataflow, including data sources, transformations, and sinks.
-
Test and debug the pipeline to ensure it meets the desired requirements.
Three other activities
Expand to see all activities and additional details
Show all six activities
Write a blog post on the benefits of using TPL Dataflow
Show steps
Summarize and share your understanding of TPL Dataflow by creating a blog post, reinforcing your knowledge and potentially helping others.
Browse courses on
Data Processing
Show steps
-
Identify the key benefits and use cases of TPL Dataflow.
-
Write a draft of the blog post, explaining the benefits and providing examples.
-
Edit and revise the blog post to ensure clarity and accuracy.
-
Publish the blog post on a platform of your choice.
Explore advanced TPL Dataflow techniques
Show steps
Expand your knowledge of TPL Dataflow by following tutorials on advanced topics, enhancing your skills and understanding.
Browse courses on
Parallel Programming
Show steps
-
Identify advanced TPL Dataflow techniques that align with your interests or career goals.
-
Find high-quality tutorials or online courses covering these techniques.
-
Follow the tutorials, practice the techniques, and apply them to your own projects.
Develop a data processing application using TPL Dataflow
Show steps
Showcase your skills and apply your knowledge by developing a complete data processing application using TPL Dataflow.
Browse courses on
Application Development
Show steps
-
Identify a real-world data processing problem that you can solve with TPL Dataflow.
-
Design and implement the application using TPL Dataflow, including data sources, transformations, and sinks.
-
Test and evaluate the application to ensure it meets the desired requirements.
-
Deploy and maintain the application in a production environment.
Review of Basic Data Structures
Show steps
Review the basic data structures covered in a previous data structures course or complete a refresher course on data structures.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on data structures
- Complete practice problems on basic data structures
Review data flow programming concepts
Show steps
Review key concepts in data flow programming to strengthen your foundational understanding before starting the course.
Browse courses on
Programming
Show steps
Show steps
Show steps
- Identify key concepts in data flow programming, such as data streams, operators, and graphs.
- Review different data flow programming models and their advantages and disadvantages.
- Practice creating simple data flow programs to gain hands-on experience.
Build a data processing pipeline with TPL Dataflow
Show steps
Gain practical experience in using TPL Dataflow to build a data processing pipeline, reinforcing the concepts learned in the course.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Design a data processing pipeline for a specific data processing task.
- Implement the pipeline using TPL Dataflow, including data sources, transformations, and sinks.
- Test and debug the pipeline to ensure it meets the desired requirements.
Three other activities
Expand to see all activities and additional details
Show all six activities
Write a blog post on the benefits of using TPL Dataflow
Show steps
Summarize and share your understanding of TPL Dataflow by creating a blog post, reinforcing your knowledge and potentially helping others.
Browse courses on
Data Processing
Show steps
Show steps
Show steps
- Identify the key benefits and use cases of TPL Dataflow.
- Write a draft of the blog post, explaining the benefits and providing examples.
- Edit and revise the blog post to ensure clarity and accuracy.
- Publish the blog post on a platform of your choice.
Explore advanced TPL Dataflow techniques
Show steps
Expand your knowledge of TPL Dataflow by following tutorials on advanced topics, enhancing your skills and understanding.
Browse courses on
Parallel Programming
Show steps
Show steps
Show steps
- Identify advanced TPL Dataflow techniques that align with your interests or career goals.
- Find high-quality tutorials or online courses covering these techniques.
- Follow the tutorials, practice the techniques, and apply them to your own projects.
Develop a data processing application using TPL Dataflow
Show steps
Showcase your skills and apply your knowledge by developing a complete data processing application using TPL Dataflow.
Browse courses on
Application Development
Show steps
Show steps
Show steps
- Identify a real-world data processing problem that you can solve with TPL Dataflow.
- Design and implement the application using TPL Dataflow, including data sources, transformations, and sinks.
- Test and evaluate the application to ensure it meets the desired requirements.
- Deploy and maintain the application in a production environment.
Career center
Learners who complete Advanced Data and Stream Processing with Microsoft TPL Dataflow will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer designs, builds, tests, and manages data pipelines. These pipelines automate the flow of data between various data sources, data processing tools, and data storage systems. As a Data Engineer, you can use your data flow programming and actor model skills, gained through this course, to design and construct efficient and scalable data pipelines. With a fit score of 72, this course may also be helpful for you in optimizing data pipelines for performance and handling large volumes of data.
Software Architect
Software Architect
A Software Architect designs and develops the overall architecture of software systems. This role requires a deep understanding of software design principles and patterns. With a fit score of 70, the data flow programming model and actor model concepts covered in this course may be useful for you in designing scalable and efficient software systems. Particularly, you may find the course's coverage of performance optimization and monitoring valuable.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. With a fit score of 68, this course may be helpful for you in developing data processing pipelines that can handle large and complex datasets. Particularly, you may find the course's coverage of parallelization and performance optimization valuable.
Big Data Architect
Big Data Architect
A Big Data Architect designs and implements data architectures for organizations that need to manage and process large volumes of data. With a fit score of 66, the data flow programming model and actor model concepts covered in this course may be useful for you in designing scalable and efficient big data architectures. Particularly, you may find the course's coverage of performance optimization and monitoring valuable.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models and applications. This role requires a strong understanding of machine learning algorithms and techniques. With a fit score of 64, this course may be helpful for you in developing data processing pipelines for training and deploying machine learning models. Particularly, you may find the course's coverage of parallelization and performance optimization valuable.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst analyzes data to identify trends, patterns, and insights that can help businesses make informed decisions. With a fit score of 62, this course may be helpful for you in developing data processing pipelines for business intelligence applications. Particularly, you may find the course's coverage of performance optimization and monitoring valuable.
Data Analyst
Data Analyst
A Data Analyst analyzes data to identify trends, patterns, and insights that can help businesses make informed decisions. With a fit score of 60, this course may be helpful for you in developing data processing pipelines for data analysis applications. Particularly, you may find the course's coverage of performance optimization and monitoring valuable.
Cloud Architect
Cloud Architect
A Cloud Architect designs and implements cloud computing solutions for organizations. This role requires a deep understanding of cloud computing technologies and services. With a fit score of 58, the data flow programming model and actor model concepts covered in this course may be useful for you in designing scalable and efficient cloud-based data processing solutions.
Software Engineer
Software Engineer
A Software Engineer designs, develops, tests, and maintains software systems. This role requires a strong understanding of software development principles and practices. With a fit score of 56, the data flow programming model and actor model concepts covered in this course may be useful for you in designing and developing scalable and efficient software systems.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This role requires a deep understanding of database systems and technologies. With a fit score of 54, the data flow programming model and actor model concepts covered in this course may be useful for you in designing and developing efficient database solutions.
Data Integration Engineer
Data Integration Engineer
A Data Integration Engineer designs and implements data integration solutions for organizations. This role requires a deep understanding of data integration technologies and tools. With a fit score of 52, the data flow programming model and actor model concepts covered in this course may be useful for you in designing and developing scalable and efficient data integration solutions.
Data Warehouse Engineer
Data Warehouse Engineer
A Data Warehouse Engineer designs and implements data warehouse solutions for organizations. This role requires a deep understanding of data warehouse technologies and tools. With a fit score of 50, the data flow programming model and actor model concepts covered in this course may be useful for you in designing and developing scalable and efficient data warehouse solutions.
Big Data Engineer
Big Data Engineer
A Big Data Engineer designs and implements big data solutions for organizations. This role requires a deep understanding of big data technologies and tools. With a fit score of 48, the data flow programming model and actor model concepts covered in this course may be useful for you in designing and developing scalable and efficient big data solutions.
Information Systems Manager
Information Systems Manager
An Information Systems Manager plans and manages the information systems of an organization. This role requires a broad understanding of information technology and business processes. With a fit score of 46, the data flow programming model and actor model concepts covered in this course may be useful for you in designing and implementing efficient information systems solutions.
Data Governance Analyst
Data Governance Analyst
A Data Governance Analyst develops and implements data governance policies and procedures for organizations. This role requires a deep understanding of data governance best practices and regulations. With a fit score of 44, the data flow programming model and actor model concepts covered in this course may be useful for you in understanding the data flow and processing requirements of data governance initiatives.
For more career information including salaries, visit:
OpenCourser.com/course/1ov4zk/advanced
Reading list
We've selected six 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
Advanced Data and Stream Processing with Microsoft TPL Dataflow.
Provides a practical guide to designing and building data-intensive applications. It covers a wide range of topics, including data modeling, data storage, data processing, and data analysis. It also discusses the challenges of building scalable and reliable data-intensive systems.
Provides a comprehensive overview of parallel computing algorithms and architectures. It covers a wide range of topics, including parallel algorithms, parallel architectures, and performance evaluation.
Save
Provides a comprehensive overview of distributed computing principles and applications. It covers a wide range of topics, including distributed systems, distributed algorithms, and distributed applications.
Provides a comprehensive overview of parallel data structures. It covers a wide range of topics, including lock-free data structures, transactional memory, and high-performance computing.
Provides a comprehensive overview of reactive programming concepts and techniques, using .NET as the primary programming language. It covers the fundamentals of reactive programming, including streams, observables, and schedulers. It also discusses advanced topics such as error handling, concurrency, and testing.
Provides a comprehensive overview of asynchronous programming with async/await in C#. It covers the basics of async/await, including tasks, async methods, and cancellation. It also discusses advanced topics such as error handling, concurrency, and testing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1ov4zk/advanced
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Advanced Data Science Capstone
Time
174 hours
Level
Advanced
Via
Pluralsight
Instructor
Szymon Warda
Language
English
Good to know
Develops an understanding of the actor model implemented in Microsoft TPL Dataflow
Teaches the data flow programming model with Microsoft TPL Dataflow
Suitable for learners who have experience with high performance systems, large data-processing systems, or Microsoft TPL Dataflow
Emphasizes performance and monitoring, which are essential concepts for large data-processing systems
May not be suitable for beginners in software development or data processing
Does not cover advanced topics such as deep learning or machine learning
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