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Google Cloud Training
Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
Register for this course and see more details by visiting:
OpenCourser.com/course/8s16wa/building
What's inside
Syllabus
Introduction
In this module, we introduce the course and agenda
Introduction to Building Batch Data Pipelines
This module reviews different methods of data loading: EL, ELT and ETL and when to use what
Read more
Syllabus
Introduction
In this module, we introduce the course and agenda
Introduction to Building Batch Data Pipelines
This module reviews different methods of data loading: EL, ELT and ETL and when to use what
Read more
Executing Spark on Dataproc
This module shows how to run Hadoop on Dataproc, how to leverage Cloud Storage, and how to optimize your Dataproc jobs.
Serverless Data Processing with Dataflow
This module covers using Dataflow to build your data processing pipelines
Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
This module shows how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Course Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines methods of data loading, including EL, ELT and ETL, and when to use each one
Teaches how to leverage Cloud Storage and optimize Dataproc jobs
Develops serverless data processing skills with Dataflow
Explores data pipeline management with Cloud Data Fusion and Cloud Composer
Provides hands-on experience building data pipeline components using Qwiklabs
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Save Building Batch Data Pipelines on Google Cloud to your list so you can find it easily later:
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Reviews summary
Batch pipeline development on google cloud
Learners say this certification-oriented course is largely positive, giving it high marks for concept explanations and practical hands-on experience. They highlight engaging assignments and the relevance of these lessons to a data engineer role in the industry. However, learners also express concerns about some difficult exams, lack of beginner-friendliness, and occasional technical issues with labs.
Learners felt that the cost of the course was reasonable given the value and skills they gained.
"The cost of the course was worth it."
"I think the course was a good value for the money."
While some learners appreciate the hands-on approach, others felt the labs should have more in-depth tasks to strengthen the learning experience.
"The hands-on labs were a great way to learn the material."
"I would have liked to see more hands-on labs."
"The labs were too easy and didn't provide enough of a challenge."
Learners see the course as well-suited for exam preparation, providing valuable content and insights.
"This course is a great way to prepare for the certification exam."
"The practice exams were particularly helpful."
The course offered a comprehensive overview of Google Cloud's features and services for managing data engineering pipelines.
"It was great to apply the code assignments to the Google Cloud tools."
"The labs have been helpful in providing hands-on experience with the Google Cloud Platform."
"The exercises and projects are very good for practicing the use of GCP services."
The course provides a high level of code learning to get you up and running with the tools from Google Cloud, which is useful for practical applications on the job.
"I am enjoying the hands-on experience, especially with the Python coding assignments."
"I was able to develop my coding skills and apply them immediately to my portfolio, which was great for getting a job afterwards."
Students find the assignments in this course to be engaging, featuring plenty of hands-on experience and chances to test your understanding through code and exercises.
"Amazing course, very engaging assignments."
"I like the engagement, continuous feedback and tests."
"The hands-on assignments are graded and are relevant to data engineering tasks"
The assumption of prior knowledge and fast pacing left some learners feeling behind, especially those new to data engineering.
"The course is not beginner-friendly."
"The pace of the course was too fast for me. I'm not a programmer, and some of the topics went over my head."
Some learners experienced technical difficulties, especially with labs, impacting their learning experience.
"The labs don't work most of the time."
"I found the course a bit disappointing. There were several technical issues I encountered that made it really difficult to engage with the material properly."
"I wish the labs ran smoothly. I spent most of my time reaching out for help support instead of actually learning."
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 Batch Data Pipelines on Google Cloud with these
activities:
Review the fundamentals of data pipelining
Show steps
Ensure that you have a strong understanding of the basic concepts and terminologies used in data pipelining.
Show steps
-
Read through the course materials on data pipelining.
-
Review articles and blog posts on the different data pipelining paradigms (EL, ELT, ETL).
-
Complete practice exercises or quizzes on data pipelining concepts.
Explore the official Qwiklabs for Google Cloud Platform
Show steps
Review some of the key tools and technologies that you will be using in this course by following their official tutorials from Qwiklabs.
Browse courses on
Cloud Storage
Show steps
-
Visit the Qwiklabs website and browsing through the available courses on Cloud Platform.
-
Select a course that covers a tool or technology that you'll be using in this course, such as Cloud Storage or Dataproc.
-
Complete the course and follow the instructions provided in the lab.
Compile a list of resources on data pipeline best practices
Show steps
Expand your knowledge by gathering and organizing resources on best practices for designing and implementing data pipelines.
Browse courses on
Data Pipeline
Show steps
-
Conduct an online search for articles, blog posts, and whitepapers on data pipeline best practices.
-
Create a document or spreadsheet to organize your findings.
-
Categorize the resources based on topics, such as data quality, performance optimization, or security.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Connect with data pipeline professionals
Show steps
Expand your network and learn from others in the field by attending meetups or virtual events.
Browse courses on
Data Pipeline
Show steps
-
Find local or online meetups or events focused on data pipeline technologies.
-
Attend the event and introduce yourself to other attendees.
-
Engage in conversations and ask questions about data pipeline practices and experiences.
Attend a workshop on data pipeline technologies
Show steps
Engage with experts and practitioners in the field by attending workshops on specific data pipeline technologies, such as BigQuery or Dataflow.
Browse courses on
Data Pipeline
Show steps
-
Search for upcoming workshops or conferences on data pipeline technologies.
-
Register for a workshop that aligns with your interests and learning goals.
-
Attend the workshop and actively participate in discussions and hands-on exercises.
Create a data pipeline diagram
Show steps
Develop a visual representation of a data pipeline to enhance your understanding of the data flow and its components.
Browse courses on
Data Pipeline
Show steps
-
Choose a data pipeline scenario or use case.
-
Identify the different stages and components of the data pipeline.
-
Create a diagram using a tool like draw.io or Visio to illustrate the data flow and interactions between components.
Solve data pipeline design problems
Show steps
Test your understanding of data pipeline design by solving real-world problems and scenarios.
Browse courses on
Data Pipeline
Show steps
-
Find online resources or platforms that provide data pipeline design challenges.
-
Select a challenge and analyze the requirements.
-
Design a data pipeline solution and document your approach.
-
Submit your solution for review and feedback.
Build a sample data pipeline using Google Cloud technologies
Show steps
Gain hands-on experience by building a small-scale data pipeline that incorporates concepts covered in the course.
Browse courses on
Data Pipeline
Show steps
-
Choose a simple data pipeline scenario or use case.
-
Select the appropriate Google Cloud technologies for your pipeline, such as BigQuery or Dataflow.
-
Develop and implement your data pipeline.
-
Test and validate your pipeline.
Review the fundamentals of data pipelining
Show steps
Ensure that you have a strong understanding of the basic concepts and terminologies used in data pipelining.
Show steps
Show steps
Show steps
- Read through the course materials on data pipelining.
- Review articles and blog posts on the different data pipelining paradigms (EL, ELT, ETL).
- Complete practice exercises or quizzes on data pipelining concepts.
Explore the official Qwiklabs for Google Cloud Platform
Show steps
Review some of the key tools and technologies that you will be using in this course by following their official tutorials from Qwiklabs.
Browse courses on
Cloud Storage
Show steps
Show steps
Show steps
- Visit the Qwiklabs website and browsing through the available courses on Cloud Platform.
- Select a course that covers a tool or technology that you'll be using in this course, such as Cloud Storage or Dataproc.
- Complete the course and follow the instructions provided in the lab.
Compile a list of resources on data pipeline best practices
Show steps
Expand your knowledge by gathering and organizing resources on best practices for designing and implementing data pipelines.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Conduct an online search for articles, blog posts, and whitepapers on data pipeline best practices.
- Create a document or spreadsheet to organize your findings.
- Categorize the resources based on topics, such as data quality, performance optimization, or security.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Connect with data pipeline professionals
Show steps
Expand your network and learn from others in the field by attending meetups or virtual events.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Find local or online meetups or events focused on data pipeline technologies.
- Attend the event and introduce yourself to other attendees.
- Engage in conversations and ask questions about data pipeline practices and experiences.
Attend a workshop on data pipeline technologies
Show steps
Engage with experts and practitioners in the field by attending workshops on specific data pipeline technologies, such as BigQuery or Dataflow.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Search for upcoming workshops or conferences on data pipeline technologies.
- Register for a workshop that aligns with your interests and learning goals.
- Attend the workshop and actively participate in discussions and hands-on exercises.
Create a data pipeline diagram
Show steps
Develop a visual representation of a data pipeline to enhance your understanding of the data flow and its components.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Choose a data pipeline scenario or use case.
- Identify the different stages and components of the data pipeline.
- Create a diagram using a tool like draw.io or Visio to illustrate the data flow and interactions between components.
Solve data pipeline design problems
Show steps
Test your understanding of data pipeline design by solving real-world problems and scenarios.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Find online resources or platforms that provide data pipeline design challenges.
- Select a challenge and analyze the requirements.
- Design a data pipeline solution and document your approach.
- Submit your solution for review and feedback.
Build a sample data pipeline using Google Cloud technologies
Show steps
Gain hands-on experience by building a small-scale data pipeline that incorporates concepts covered in the course.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Choose a simple data pipeline scenario or use case.
- Select the appropriate Google Cloud technologies for your pipeline, such as BigQuery or Dataflow.
- Develop and implement your data pipeline.
- Test and validate your pipeline.
Career center
Learners who complete Building Batch Data Pipelines on Google Cloud will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines for an organization. They collaborate with Data Scientists and other stakeholders to gather requirements, design and build data pipelines, and ensure that the data is accurate, complete, and timely. This course is a valuable resource for anyone looking to become a Data Engineer as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Data Architect
Data Architect
A Data Architect designs and manages an organization's data architecture. They work with stakeholders to understand their data needs, design and build data pipelines, and ensure that the data is accurate, complete, and timely. This course is a valuable resource for anyone looking to become a Data Architect as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. They collaborate with stakeholders to understand their business needs, gather data, analyze data, and build models to make predictions. This course is a valuable resource for anyone looking to become a Data Scientist as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Business Analyst
Business Analyst
A Business Analyst gathers and analyzes data to help businesses make informed decisions. They work with stakeholders to understand their business needs, gather data, analyze data, and make recommendations. This course is a valuable resource for anyone looking to become a Business Analyst as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Cloud Architect
Cloud Architect
A Cloud Architect designs and manages an organization's cloud infrastructure. They work with stakeholders to understand their business needs, design and build cloud infrastructure, and ensure that the infrastructure is scalable, reliable, and secure. This course is a valuable resource for anyone looking to become a Cloud Architect as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Database Administrator
Database Administrator
A Database Administrator manages an organization's databases. They work with stakeholders to understand their data needs, design and build databases, and ensure that the databases are performant, reliable, and secure. This course is a valuable resource for anyone looking to become a Database Administrator as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Software Engineer
Software Engineer
A Software Engineer designs, builds, and maintains software systems. They work with stakeholders to understand their business needs, design and build software systems, and ensure that the software systems are scalable, reliable, and secure. This course may be useful for anyone looking to become a Software Engineer as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Systems Administrator
Systems Administrator
A Systems Administrator manages an organization's computer systems. They work with stakeholders to understand their business needs, design and build computer systems, and ensure that the computer systems are performant, reliable, and secure. This course may be useful for anyone looking to become a Systems Administrator as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Data Analyst
Data Analyst
A Data Analyst gathers and analyzes data to help businesses make informed decisions. They work with stakeholders to understand their business needs, gather data, analyze data, and make recommendations. This course may be useful for anyone looking to become a Data Analyst as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Information Security Analyst
Information Security Analyst
An Information Security Analyst protects an organization's information systems from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with stakeholders to understand their security needs, design and implement security controls, and monitor and respond to security incidents. This course may be useful for anyone looking to become an Information Security Analyst as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Statistician
Statistician
A Statistician uses statistical methods to collect, analyze, interpret, and present data. They work with stakeholders to understand their research needs, design and conduct statistical studies, and analyze and interpret data. This course may be useful for anyone looking to become a Statistician as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical methods to solve business problems. They work with stakeholders to understand their business needs, develop and implement mathematical models, and analyze and interpret results. This course may be useful for anyone looking to become an Operations Research Analyst as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Financial Analyst
Financial Analyst
A Financial Analyst uses financial data to evaluate and make investment decisions. They work with stakeholders to understand their investment goals, analyze financial data, and make recommendations. This course may be useful for anyone looking to become a Financial Analyst as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Market Research Analyst
Market Research Analyst
A Market Research Analyst gathers and analyzes data to understand consumer behavior. They work with stakeholders to understand their marketing needs, design and conduct market research studies, and analyze and interpret data. This course may be useful for anyone looking to become a Market Research Analyst as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
Product Manager
Product Manager
A Product Manager manages the development and launch of a product. They work with stakeholders to understand their product needs, design and develop the product, and launch and market the product. This course may be useful for anyone looking to become a Product Manager as it provides a comprehensive overview of the different methods of data loading, how to execute Spark on Dataproc, how to use Dataflow for serverless data processing, and how to manage data pipelines with Cloud Data Fusion and Cloud Composer.
For more career information including salaries, visit:
OpenCourser.com/course/8s16wa/building
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
Building Batch Data Pipelines on Google Cloud.
Is specifically tailored to building data pipelines on Google Cloud Platform. It provides detailed guidance on using Cloud Dataflow, BigQuery, and other Google Cloud services for data transformation and management.
Provides a practical guide to cloud data engineering, covering topics such as data architecture, data governance, and data processing. It offers insights into the challenges and opportunities of building data pipelines in the cloud.
This definitive guide to Apache Spark, providing a comprehensive overview of the Spark ecosystem. It covers topics such as data storage, data processing, and data analysis, and offers insights into the challenges and opportunities of working with Spark.
Focuses on using Apache Airflow for data pipeline orchestration. It provides detailed guidance on designing, building, and maintaining data pipelines with Airflow, and offers practical examples of real-world use cases.
Provides a comprehensive introduction to deep learning with Python, covering topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). It offers practical guidance on building and training deep learning models using popular Python libraries such as TensorFlow and Keras.
Provides a comprehensive overview of stream processing with Apache Flink. It covers topics such as stream ingestion, transformation, and analysis, and offers practical examples of building stream processing applications with Flink.
This classic book on Hadoop, providing a comprehensive overview of the Hadoop ecosystem. It covers topics such as data storage, data processing, and data analysis, and offers insights into the challenges and opportunities of working with Hadoop.
Provides a practical introduction to data science for business professionals. It covers topics such as data analysis, data mining, and machine learning, and offers insights into the challenges and opportunities of data-driven decision-making.
Provides a broad overview of big data management and analytics. It covers topics such as data warehousing, data mining, and machine learning, and offers insights into the challenges and opportunities of working with big data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8s16wa/building
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Examines methods of data loading, including EL, ELT and ETL, and when to use each one
Teaches how to leverage Cloud Storage and optimize Dataproc jobs
Develops serverless data processing skills with Dataflow
Explores data pipeline management with Cloud Data Fusion and Cloud Composer
Provides hands-on experience building data pipeline components using Qwiklabs
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