Serverless Data Processing with Dataflow: Foundations from Coursera

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

Prerequisites:

The Serverless Data Processing with Dataflow course series builds on the concepts covered in the Data Engineering specialization. We recommend the following prerequisite courses:

(i)Building batch data pipelines on Google Cloud : covers core Dataflow principles

(ii)Building Resilient Streaming Analytics Systems on Google Cloud : covers streaming basics concepts like windowing, triggers, and watermarks

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What's inside

Syllabus

Introduction

This module covers the course outline and does a quick refresh on the Apache Beam programming model and Google’s Dataflow managed service.

Beam Portability

In this module we are going to learn about four sections, Beam Portablity, Runner v2, Container Environments, and Cross-Language Transforms.

Separating Compute and Storage with Dataflow

In this module we discuss how to separate compute and storage with Dataflow. This module contains four sections Dataflow, Dataflow Shuffle Service, Dataflow Streaming Engine, Flexible Resource Scheduling.

IAM, Quotas, and Permissions

In this module, we talk about the different IAM roles, quotas, and permissions required to run Dataflow

Security

In this module, we will look at how to implement the right security model for your use case on Dataflow.

Summary

In this course, we started with the refresher of what Apache Beam is, and its relationship with Dataflow.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by Google Cloud Training, a recognized source for expertise in the field

Provides valuable insights into real-world applications of serverless data processing with Dataflow

Suitable for experienced developers and data engineers seeking to enhance their skills in serverless data processing

Requires prior knowledge in Apache Beam and Dataflow. Completing the recommended prerequisite courses is highly recommended

Course content may be subject to change based on advancements in the field

Reviews summary

Dataflow fundamentals concepts

Learners say that this course offers a **good** summary of serverless data processing, providing comprehensive content with a clear focus on the key points. Practical exercises enhance understanding, making it a valuable choice for those interested in mastering Dataflow. Highly recommended for skill enhancement.

Focuses on key points.

"The course "Serverless Data Processing with Dataflow: Foundations" offers a good summarize of serverless data processing, providing comprehensive content with a clear focus on the key points."

"It is what they say, it builds on the Data and ML Engineer Specialization, it was good for someone like me who already has taken quite a few qwiklabs labs and knows how to work around GCloud console, for starters I would recommend to take any other fundamental courses that cover basics of dataflow and then try this."

Good practical labs for beginners.

"More labs should be designed to help learners internalise the knowledge."

"Having detailed information will help learners learn quickly"

May require background knowledge.

"It would be better having detailed explanation of concepts for very beginners."

"This is a great course. Having detailed information will help learners learn quickly"

May require GCP 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 Serverless Data Processing with Dataflow: Foundations with these activities:

Refresh knowledge of Apache Beam

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Introduce yourself to concepts and techniques you should be familiar with before going into this course.

Browse courses on Apache Beam

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Review Apache Beam documentation
Take a short online tutorial on Apache Beam
Go through a code lab or sample Apache Beam project

Read 'Designing Data-Intensive Applications'

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Reinforce foundational knowledge of Apache Beam pipelines and data processing.

View Secret Colors on Amazon

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Read the first 5 chapters of the book
Summarize the key concepts and techniques

Follow Apache Beam tutorials

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Gain hands-on experience with Apache Beam pipelines.

Browse courses on Apache Beam

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Go through the official Apache Beam tutorials
Complete a few code labs or sample Apache Beam projects

Five other activities

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Show all eight activities

Join an Apache Beam community forum or discussion group

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Engage with peers and experts to enhance understanding of Apache Beam.

Browse courses on Apache Beam

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Join an online Apache Beam community forum or discussion group
Ask questions and participate in discussions

Build a simple Apache Beam pipeline

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Apply your knowledge of Apache Beam to a practical project.

Browse courses on Apache Beam

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Identify a simple data processing task
Design and implement an Apache Beam pipeline to perform the task
Test and evaluate the performance of the pipeline

Attend an Apache Beam workshop or conference

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Gain in-depth knowledge and learn best practices from Apache Beam experts.

Browse courses on Apache Beam

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Identify relevant Apache Beam workshops or conferences
Register and attend the event

Contribute to the Apache Beam open-source project

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Deepen your understanding of Apache Beam and make valuable contributions to the community.

Browse courses on Apache Beam

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Identify an area of the Apache Beam project to contribute to
Submit a code change or documentation improvement

Mentor others in the field of serverless data processing

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Solidify your knowledge by teaching others and helping them overcome challenges.

Browse courses on Apache Beam

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Identify opportunities to mentor others
Provide guidance and support to your mentees

Career center

Learners who complete Serverless Data Processing with Dataflow: Foundations will develop knowledge and skills that may be useful to these careers:

Technical Account Manager

Technical Account Managers manage relationships with clients and help them get the most out of their technology investments. This may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for Technical Account Manager

Technology Consultant

Technology Consultants advise clients on how to use technology to achieve their business goals. This may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for Technology Consultant

Technical Program Manager

Technical Program Managers oversee the development and launch of new products. This may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for Technical Program Manager

Project Manager

Project Managers are responsible for planning, executing, and closing projects. This may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for Project Manager

Product Manager

Product Managers are responsible for managing the development and launch of new products. This may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for Product Manager

Data Engineer

As a Data Engineer, you will be designing, building, and maintaining data pipelines. These pipelines may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for Data Engineer

DevOps Engineer

DevOps Engineers are responsible for building and maintaining the infrastructure that supports software development and deployment. This infrastructure may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for DevOps Engineer

Cloud Engineer

As a Cloud Engineer, you will be building and maintaining cloud-based systems. These systems may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam. Dataflow can help you save money by separating compute and storage, and it offers a variety of security features to protect your data.

See salaries and explore the career path for Cloud Engineer

Security Engineer

Security Engineers are responsible for protecting an organization's data and systems. This may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam. Dataflow offers a variety of security features to protect your data.

See salaries and explore the career path for Security Engineer

Software Developer

Software Developers design, code, and test software systems. These systems may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for Software Developer

Software Architect

Software Architects design and develop software systems. These systems may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for Software Architect

Systems Engineer

Systems Engineers design, build, and maintain computer systems. These systems may include data processing pipelines. This course may be useful if you wish to learn how to implement these pipelines with Dataflow, Google's managed service for Apache Beam.

See salaries and explore the career path for Systems Engineer

Data Scientist

Data Scientists use data to build models that can be used to predict future outcomes or make recommendations. This course may be useful if you wish to learn how to use Dataflow to build data processing pipelines. These pipelines can be used to automate the process of data collection, cleaning, and analysis, freeing up your time to focus on more strategic tasks.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. These models may be used to predict future outcomes or make recommendations. This course may be useful if you wish to learn how to use Dataflow to build data processing pipelines. These pipelines can be used to automate the process of data collection, cleaning, and analysis, freeing up your time to focus on more strategic tasks.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst is responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course may be useful if you wish to learn how to use Dataflow to build data processing pipelines. These pipelines can be used to automate the process of data collection, cleaning, and analysis, freeing up your time to focus on more strategic tasks.

See salaries and explore the career path for Data Analyst