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Janani Ravi

Dataflow represents a fundamentally different approach to Big Data processing than computing engines such as Spark. Dataflow is serverless and fully-managed, and supports running pipelines designed using Apache Beam APIs.

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Dataflow represents a fundamentally different approach to Big Data processing than computing engines such as Spark. Dataflow is serverless and fully-managed, and supports running pipelines designed using Apache Beam APIs.

Dataflow allows developers to process and transform data using easy, intuitive APIs. Dataflow is built on the Apache Beam architecture and unifies batch as well as stream processing of data. In this course, Conceptualizing the Processing Model for the GCP Dataflow Service, you will be exposed to the full potential of Cloud Dataflow and its innovative programming model.

First, you will work with an example Apache Beam pipeline performing stream processing operations and see how it can be executed using the Cloud Dataflow runner.

Next, you will understand the basic optimizations that Dataflow applies to your execution graph such as fusion and combine optimizations.

Finally, you will explore Dataflow pipelines without writing any code at all using built-in templates. You will also see how you can create a custom template to execute your own processing jobs.

When you are finished with this course, you will have the skills and knowledge to design Dataflow pipelines using Apache Beam SDKs, integrate these pipelines with other Google services, and run these pipelines on the Google Cloud Platform.

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What's inside

Syllabus

Course Overview
Getting Started with Cloud Dataflow
Monitoring Jobs in Cloud Dataflow
Optimizing Cloud Dataflow Pipelines
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Running Cloud Dataflow Pipelines Using Templates

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops programming and data engineering skills using the Apache Beam SDKs, which are in high demand across industries
Provides hands-on experience with Cloud Dataflow's advanced features, such as fusion and combine optimizations, giving learners a competitive edge
Taught by Janani Ravi, a recognized expert in the field of data engineering and Apache Beam
Covers the full potential of Cloud Dataflow, allowing learners to leverage its capabilities for efficient data processing and transformation
Provides a strong foundation for building and deploying complex data pipelines using Google Cloud Platform

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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 Conceptualizing the Processing Model for the GCP Dataflow Service with these activities:
Participate in a study group to discuss Dataflow concepts and pipelines
Engage with peers to enhance your understanding by discussing Dataflow concepts and sharing insights.
Show steps
  • Form a study group with classmates or fellow learners
  • Meet regularly to discuss course materials, share experiences, and collaborate on projects
  • Prepare discussion topics and facilitate the sessions
  • Provide feedback and support to group members
Write code examples for the Apache Beam pipeline
Practice implementing Apache Beam pipelines to enhance your understanding of their syntax and structure.
Browse courses on Apache Beam
Show steps
  • Set up your development environment with Apache Beam and Cloud Dataflow
  • Create a simple pipeline that performs a basic transformation
  • Add more complex transformations and operations to your pipeline
  • Test and debug your pipeline to ensure it operates as intended
Attend a Dataflow workshop to deepen your understanding
Immerse yourself in the subject matter by attending a specialized Dataflow workshop led by experts.
Browse courses on Cloud Dataflow
Show steps
  • Research and identify relevant Dataflow workshops
  • Register and attend the workshop
  • Actively participate in the sessions and engage with the instructors
  • Take notes and document your learnings
Four other activities
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Show all seven activities
Follow online tutorials to implement advanced Dataflow features
Enhance your practical skills by exploring online tutorials that demonstrate advanced Dataflow features.
Browse courses on Cloud Dataflow
Show steps
  • Search for online tutorials on specific Dataflow features
  • Select tutorials that align with your learning goals
  • Follow the tutorials step-by-step to implement the features
  • Experiment with different configurations and parameters to observe their effects
Design a Dataflow pipeline architecture for a specific use case
Engage in practical application by designing a Dataflow pipeline that addresses a real-world data processing need.
Browse courses on Cloud Dataflow
Show steps
  • Identify a specific data processing problem or use case
  • Define the input data sources and output requirements
  • Design the pipeline architecture using Apache Beam primitives
  • Consider optimizations and best practices for scalability and performance
  • Document your design and share it with others
Develop a custom Dataflow template for a specific data processing task
Build a reusable component by creating a custom Dataflow template tailored to a specific data processing task.
Browse courses on Custom Templates
Show steps
  • Identify a common data processing task that can be automated
  • Design the template architecture using Apache Beam primitives
  • Implement the template using the Dataflow SDK
  • Configure the template parameters for flexibility and customization
  • Test and validate the template to ensure it meets the requirements
Contribute to an open-source Dataflow project or library
Gain hands-on experience and make a meaningful contribution by participating in the development of open-source Dataflow software.
Show steps
  • Identify an open-source Dataflow project or library that aligns with your interests
  • Review the project documentation and codebase
  • Identify areas where you can contribute or improve the software
  • Submit pull requests with your contributions
  • Collaborate with other contributors and maintainers

Career center

Learners who complete Conceptualizing the Processing Model for the GCP Dataflow Service will develop knowledge and skills that may be useful to these careers:
Data Engineer
As a Data Engineer, you'll be developing and maintaining systems for processing large amounts of data. You'll need to be able to understand the different types of data and how to process it efficiently. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in data processing by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
Data Scientist
Data Scientists use data to solve complex problems. They need to be able to understand the different types of data and how to analyze it to extract insights. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in data processing by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
Software Engineer
Software Engineers design, develop, and maintain software applications. They need to be able to understand the different types of software and how to develop them efficiently. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in software development by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
Cloud Architect
Cloud Architects design and manage cloud computing solutions. They need to be able to understand the different types of cloud services and how to use them to build scalable and efficient applications. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in cloud computing by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
Data Analyst
Data Analysts use data to make informed decisions. They need to be able to understand the different types of data and how to analyze it to extract insights. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in data analysis by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
Machine Learning Engineer
Machine Learning Engineers develop and maintain machine learning models. They need to be able to understand the different types of machine learning models and how to train and evaluate them. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in machine learning by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
DevOps Engineer
DevOps Engineers collaborate with developers and operations teams to ensure that software applications are deployed and managed efficiently. They need to be able to understand the different types of software development and operations tools and how to use them to automate and streamline the software development process. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in DevOps by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
Systems Engineer
Systems Engineers design, develop, and maintain computer systems. They need to be able to understand the different types of computer systems and how to configure and manage them. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in systems engineering by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
Network Engineer
Network Engineers design, develop, and maintain computer networks. They need to be able to understand the different types of computer networks and how to configure and manage them. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in network engineering by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
Security Engineer
Security Engineers design, develop, and maintain security systems. They need to be able to understand the different types of security systems and how to configure and manage them. The Conceptualizing the Processing Model for the GCP Dataflow Service course can help you build a foundation in security engineering by teaching you about the Apache Beam architecture and how to use it to design and execute Dataflow pipelines.
Database Administrator
Database Administrators design, develop, and maintain databases. They need to be able to understand the different types of databases and how to configure and manage them. The Conceptualizing the Processing Model for the GCP Dataflow Service course may be useful for you if you want to learn more about data processing and how to use Apache Beam to design and execute Dataflow pipelines.
Business Analyst
Business Analysts use data to make informed decisions. They need to be able to understand the different types of data and how to analyze it to extract insights. The Conceptualizing the Processing Model for the GCP Dataflow Service course may be useful for you if you want to learn more about data processing and how to use Apache Beam to design and execute Dataflow pipelines.
Project Manager
Project Managers plan and manage projects. They need to be able to understand the different types of projects and how to manage them effectively. The Conceptualizing the Processing Model for the GCP Dataflow Service course may be useful for you if you want to learn more about data processing and how to use Apache Beam to design and execute Dataflow pipelines.
Product Manager
Product Managers design and manage products. They need to be able to understand the different types of products and how to develop and market them. The Conceptualizing the Processing Model for the GCP Dataflow Service course may be useful for you if you want to learn more about data processing and how to use Apache Beam to design and execute Dataflow pipelines.
Marketing Manager
Marketing Managers plan and manage marketing campaigns. They need to be able to understand the different types of marketing campaigns and how to develop and execute them. The Conceptualizing the Processing Model for the GCP Dataflow Service course may be useful for you if you want to learn more about data processing and how to use Apache Beam to design and execute Dataflow pipelines.

Reading list

We've selected nine 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 Conceptualizing the Processing Model for the GCP Dataflow Service.
Although this book does not specifically cover Dataflow, it provides a solid foundation for understanding the principles of designing data-intensive applications. It covers topics such as data modeling, data storage, and processing, which are essential concepts for working with Dataflow.
Beginner-friendly guide to Dataflow. It covers topics such as creating and executing pipelines, using Dataflow with other Google Cloud services, and monitoring Dataflow jobs.
Provides a comprehensive guide to big data processing with Apache Hadoop. It covers topics such as the Hadoop ecosystem, data storage, and data processing. The book can be used as a textbook to teach a course on big data processing.
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as supervised and unsupervised learning, classification, and regression. The book can be used as a textbook to teach a course on pattern recognition and machine learning.
Provides a comprehensive overview of data mining. It covers topics such as data preprocessing, data mining algorithms, and data mining applications. The book can be used as a textbook to teach a course on data mining.
Provides a comprehensive overview of cloud computing. It covers topics such as cloud computing concepts, cloud computing technologies, and cloud computing architecture. The book can be used as a textbook to teach a course on cloud computing.
Provides a comprehensive guide to Hadoop. It covers topics such as the Hadoop ecosystem, data storage, and data processing. The book can be used as a textbook to teach a course on Hadoop.
Provides a comprehensive guide to Spark. It covers topics such as the Spark architecture, Spark programming, and Spark applications. The book can be used as a textbook to teach a course on Spark.
Provides a comprehensive guide to Samza. It covers topics such as the Samza architecture, Samza programming, and Samza applications. The book can be used as a textbook to teach a course on Samza.

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