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Google Cloud Training
Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.
Register for this course and see more details by visiting:
OpenCourser.com/course/3z50d0/building
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What's inside
Syllabus
Introduction
This module introduces the course and agenda
Introduction to Processing Streaming Data
This modules talks about challenges with processing streaming data
Read more
Syllabus
Introduction
This module introduces the course and agenda
Introduction to Processing Streaming Data
This modules talks about challenges with processing streaming data
Read more
Serverless Messaging with Pub/Sub
This module talks about using Pub/Sub to ingest incoming streaming data
Dataflow Streaming Features
This module revisits Dataflow and focuses on its streaming data processing capabilities
High-Throughput BigQuery and Bigtable Streaming Features
This modules covers BigQuery and Bigtable for streaming data
Advanced BigQuery Functionality and Performance
This module dives into more advanced features of BigQuery
Summary
This module recaps the topics covered in course
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches learners how to build streaming data pipelines on Google Cloud
Delves into advanced features of BigQuery, assisting learners in optimizing their data storage and analysis capabilities
Emphasizes hands-on experience by utilizing QwikLabs, enabling learners to apply their knowledge in a practical setting
Addresses the challenges associated with streaming data processing, providing learners with a comprehensive understanding of potential obstacles
Aligned with industry practices, utilizing Pub/Sub for handling incoming streaming data, ensuring learners are equipped with up-to-date knowledge
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Reviews summary
Recommended google cloud data engineering course
Learners say this course on building resilient streaming analytics systems on Google Cloud gets largely positive reviews. It engages learners with various hands-on labs, where learners praise the real-world applicability of concepts and the usefulness of the Qwiklabs. The course is said to be well-structured and practical, offering a comprehensive overview of the subject matter. However, some learners have noted that certain instructors can be difficult to understand and that some labs may be difficult to complete due to bugs or outdated materials.
The course offers engaging and informative content, with real-world examples and practical labs.
"A great introduction course to the GCP services that makes easier the building of streaming systems."
"Clair et completC'est parce qu'il y a ce genre de contenu que je progresse beaucoup plus vite sur GCP que sur les autres plateformes."
"Great labs to make the course very useful!"
The course's labs are highly praised for providing practical experience and real-world applicability.
"I really enjoyed this course. There is ample time in the labs to experiment and get familiar with the concepts and techniques being taught in this course."
"The labs are very useful"
"Many opportunities to use Cloud Dataflow and effective ways of using Google's big data warehouse, BigQuery."
Some learners have reported difficulty in understanding certain instructors and completing labs due to bugs or outdated materials.
"Sometimes the presenter in week two spoke unclear."
"The videos or Nitis are extremely unintelligeble and the trascripts are just not helping much."
"Few labs with bugs that didn't allow me to complete the course for a while.."
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 Building Resilient Streaming Analytics Systems on Google Cloud with these
activities:
Review Python Fundamentals
Show steps
Ensure a strong foundation in Python, which is essential for working with Dataflow and other streaming data technologies.
Browse courses on
Python
Show steps
-
Review basic syntax and data structures.
-
Complete a few practice problems.
Review Basic Data Analysis Concepts
Show steps
Refresh your understanding of data analysis concepts, which will help you better understand and apply streaming data techniques.
Browse courses on
Data Analysis
Show steps
-
Review descriptive statistics.
-
Review inferential statistics.
Review Data Science from Scratch
Show steps
Review the foundational concepts and techniques of data science, providing a strong foundation for the course.
View
Data Science from Scratch: First Principles...
on Amazon
Show steps
-
Read chapters 1-3.
-
Complete the exercises in chapters 1-3.
-
Summarize the key concepts from chapters 1-3.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a Simple Data Pipeline with Pub/Sub
Show steps
Gain hands-on experience with the fundamental components of a data pipeline by building your own.
Browse courses on
Pub/Sub
Show steps
-
Create a Pub/Sub topic and subscription.
-
Write a Python script to publish data to the topic.
-
Create a Dataflow pipeline to process the data from the subscription.
-
Write the data to a BigQuery table.
-
Test and iterate on your pipeline.
Follow Google Cloud Training's 'Building Streaming Data Pipelines' Guide
Show steps
Reinforce the concepts covered in the course by following a structured guide provided by the course instructors.
Browse courses on
Streaming Data Pipelines
Show steps
-
Read the guide.
-
Complete the hands-on labs.
-
Review the sample code and documentation.
Solve Dataflow Streaming Coding Challenges
Show steps
Enhance your understanding of Dataflow's streaming capabilities by solving coding challenges.
Browse courses on
Dataflow
Show steps
-
Find coding challenges online or on platforms like HackerRank.
-
Attempt to solve the challenges.
-
Review the solutions and learn from your mistakes.
Write a Blog Post on a Streaming Data Pipeline Technique
Show steps
Deepen your understanding and share your knowledge by writing about a specific technique used in streaming data pipelines.
Show steps
-
Choose a specific technique.
-
Research and gather information.
-
Write a blog post explaining the technique.
-
Share your blog post.
Contribute to a Dataflow Open-Source Project
Show steps
Gain practical experience and contribute to the streaming data ecosystem by participating in an open-source project.
Browse courses on
Open Source
Show steps
-
Find an open-source project related to Dataflow.
-
Identify an issue or feature to work on.
-
Fork the project and make your changes.
-
Submit a pull request.
-
Review feedback and iterate on your contribution.
Review Python Fundamentals
Show steps
Ensure a strong foundation in Python, which is essential for working with Dataflow and other streaming data technologies.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic syntax and data structures.
- Complete a few practice problems.
Review Basic Data Analysis Concepts
Show steps
Refresh your understanding of data analysis concepts, which will help you better understand and apply streaming data techniques.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Review descriptive statistics.
- Review inferential statistics.
Review Data Science from Scratch
Show steps
Review the foundational concepts and techniques of data science, providing a strong foundation for the course.
View
Data Science from Scratch: First Principles...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3.
- Complete the exercises in chapters 1-3.
- Summarize the key concepts from chapters 1-3.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a Simple Data Pipeline with Pub/Sub
Show steps
Gain hands-on experience with the fundamental components of a data pipeline by building your own.
Browse courses on
Pub/Sub
Show steps
Show steps
Show steps
- Create a Pub/Sub topic and subscription.
- Write a Python script to publish data to the topic.
- Create a Dataflow pipeline to process the data from the subscription.
- Write the data to a BigQuery table.
- Test and iterate on your pipeline.
Follow Google Cloud Training's 'Building Streaming Data Pipelines' Guide
Show steps
Reinforce the concepts covered in the course by following a structured guide provided by the course instructors.
Browse courses on
Streaming Data Pipelines
Show steps
Show steps
Show steps
- Read the guide.
- Complete the hands-on labs.
- Review the sample code and documentation.
Solve Dataflow Streaming Coding Challenges
Show steps
Enhance your understanding of Dataflow's streaming capabilities by solving coding challenges.
Browse courses on
Dataflow
Show steps
Show steps
Show steps
- Find coding challenges online or on platforms like HackerRank.
- Attempt to solve the challenges.
- Review the solutions and learn from your mistakes.
Write a Blog Post on a Streaming Data Pipeline Technique
Show steps
Deepen your understanding and share your knowledge by writing about a specific technique used in streaming data pipelines.
Show steps
Show steps
Show steps
- Choose a specific technique.
- Research and gather information.
- Write a blog post explaining the technique.
- Share your blog post.
Contribute to a Dataflow Open-Source Project
Show steps
Gain practical experience and contribute to the streaming data ecosystem by participating in an open-source project.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source project related to Dataflow.
- Identify an issue or feature to work on.
- Fork the project and make your changes.
- Submit a pull request.
- Review feedback and iterate on your contribution.
Career center
Learners who complete Building Resilient Streaming Analytics Systems on Google Cloud will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and managing data processing pipelines. This course provides a foundation in the tools and technologies used by Data Engineers, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to build streaming data pipelines that can process real-time data and provide valuable insights to businesses.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems and make informed decisions. This course provides a foundation in the tools and technologies used by Data Analysts, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to analyze streaming data and provide valuable insights to businesses.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. This course provides a foundation in the tools and technologies used by Machine Learning Engineers, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to build streaming data pipelines that can collect and process data in real time, enabling you to build more accurate and timely models.
Data Scientist
Data Scientist
Data Scientists use data to build models that can predict future outcomes. This course provides a foundation in the tools and technologies used by Data Scientists, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to build streaming data pipelines that can collect and process data in real time, enabling you to build more accurate and timely models.
Data Integration Engineer
Data Integration Engineer
Data Integration Engineers design and build data integration solutions. This course provides a foundation in the tools and technologies used by Data Integration Engineers, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to build streaming data pipelines that can process real-time data and provide valuable insights to businesses.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software applications. This course provides a foundation in the tools and technologies used by Software Engineers, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to build streaming data pipelines that can process real-time data and provide valuable insights to businesses.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing environments. This course provides a foundation in the tools and technologies used by Cloud Architects, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to build streaming data pipelines that can process real-time data and provide valuable insights to businesses.
DevOps Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams. This course provides a foundation in the tools and technologies used by DevOps Engineers, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to build streaming data pipelines that can process real-time data and provide valuable insights to businesses.
Systems Engineer
Systems Engineer
Systems Engineers design and manage complex systems. This course provides a foundation in the tools and technologies used by Systems Engineers, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to build streaming data pipelines that can process real-time data and provide valuable insights to businesses.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses make better decisions. This course provides a foundation in the tools and technologies used by Business Intelligence Analysts, including Pub/Sub, Dataflow, BigQuery, and Bigtable. By completing this course, you will gain the skills needed to analyze streaming data and provide valuable insights to businesses.
Sales Engineer
Sales Engineer
Sales Engineers help customers understand and purchase products and services. This course may be useful for Sales Engineers who are interested in learning about the tools and technologies used to build streaming data pipelines. By completing this course, you will gain a better understanding of the challenges and opportunities associated with processing streaming data.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for software and hardware products. This course may be useful for Technical Writers who are interested in learning about the tools and technologies used to build streaming data pipelines. By completing this course, you will gain a better understanding of the challenges and opportunities associated with processing streaming data.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. This course may be useful for Product Managers who are interested in learning about the tools and technologies used to build streaming data pipelines. By completing this course, you will gain a better understanding of the challenges and opportunities associated with processing streaming data.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to help businesses understand their customers and develop marketing campaigns. This course may be useful for Marketing Analysts who are interested in learning about the tools and technologies used to build streaming data pipelines. By completing this course, you will gain a better understanding of the challenges and opportunities associated with processing streaming data.
Project Manager
Project Manager
Project Managers plan and execute projects. This course may be useful for Project Managers who are interested in learning about the tools and technologies used to build streaming data pipelines. By completing this course, you will gain a better understanding of the challenges and opportunities associated with processing streaming data.
For more career information including salaries, visit:
OpenCourser.com/course/3z50d0/building
Reading list
We've selected ten 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
Building Resilient Streaming Analytics Systems on Google Cloud.
Provides a comprehensive overview of large-scale machine learning with TensorFlow. It covers the basics of TensorFlow, as well as more advanced topics such as distributed training, model optimization, and serving.
Provides a comprehensive overview of deep learning with Python. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a hands-on introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers the basics of machine learning, as well as more advanced topics such as data preprocessing, model selection, and hyperparameter tuning.
Provides a comprehensive overview of data-intensive text processing with MapReduce. It covers the basics of MapReduce, as well as more advanced topics such as natural language processing, information retrieval, and machine learning.
Provides a comprehensive overview of natural language processing with Python. It covers the basics of natural language processing, as well as more advanced topics such as machine learning, deep learning, and information retrieval.
Provides a comprehensive overview of speech and language processing. It covers the basics of speech and language processing, as well as more advanced topics such as machine learning, deep learning, and natural language processing.
Save
Provides a comprehensive overview of computer vision. It covers the basics of computer vision, as well as more advanced topics such as machine learning, deep learning, and image processing.
Provides a comprehensive overview of pattern recognition and machine learning. It covers the basics of pattern recognition and machine learning, as well as more advanced topics such as statistical learning, Bayesian inference, and neural networks.
Save
Provides a comprehensive overview of machine learning from an algorithmic perspective. It covers the basics of machine learning, as well as more advanced topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of data science for business. It covers the basics of data science, as well as more advanced topics such as data mining, machine learning, and data visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3z50d0/building
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Via
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Institution
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Instructor
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Language
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Good to know
Teaches learners how to build streaming data pipelines on Google Cloud
Delves into advanced features of BigQuery, assisting learners in optimizing their data storage and analysis capabilities
Emphasizes hands-on experience by utilizing QwikLabs, enabling learners to apply their knowledge in a practical setting
Addresses the challenges associated with streaming data processing, providing learners with a comprehensive understanding of potential obstacles
Aligned with industry practices, utilizing Pub/Sub for handling incoming streaming data, ensuring learners are equipped with up-to-date knowledge
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