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ETL Processing on Google Cloud Using Dataflow and BigQuery

Google Cloud Training

This is a self-paced lab that takes place in the Google Cloud console.

In this lab you will build several Data Pipelines that will ingest data from a publicly available dataset into BigQuery.

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What's inside

Syllabus

ETL Processing on Google Cloud Using Dataflow and BigQuer

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Applies concepts in data engineering

Applies Apache Beam tool for data engineering

Uses Google Cloud Platform tools

Employs the BigQuery interface for database operations

Learners should have foundational knowledge in data engineering and working knowledge of cloud platforms, BigQuery, and Apache Beam

Course requires access to Google Cloud Platform and BigQuery, which can require payment to use

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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 ETL Processing on Google Cloud Using Dataflow and BigQuery with these activities:

Review SQL fundamentals

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Reviewing SQL fundamentals will strengthen your understanding of the language and prepare you for the course's data manipulation and querying tasks.

Browse courses on SQL

Show steps

Go through online SQL tutorials or documentation
Practice writing basic SQL queries
Solve SQL coding challenges

Read 'Designing Data-Intensive Applications' by Martin Kleppmann

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Reading this book will provide you with a comprehensive understanding of the principles and practices of designing and building data-intensive applications.

View Secret Colors on Amazon

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Obtain a copy of the book
Read through the book, taking notes and highlighting important concepts
Discuss the book's content with peers or mentors

Explore Google Cloud BigQuery documentation

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Familiarizing yourself with BigQuery documentation will provide you with a solid foundation for using the platform in this course.

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Read through the BigQuery documentation
Follow along with BigQuery tutorials
Create a BigQuery project and explore its features

Four other activities

Expand to see all activities and additional details

Show all seven activities

Participate in online discussion forums

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Engaging in discussion forums will allow you to connect with other learners, share knowledge, and seek clarification on course-related topics.

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Join online discussion forums related to Dataflow and BigQuery
Participate in discussions, ask questions, and provide answers
Engage with other learners and experts in the field

Complete Dataflow and BigQuery coding exercises

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Engaging in coding exercises will enhance your practical skills in using Dataflow and BigQuery for data processing and manipulation.

Browse courses on Dataflow

Show steps

Find online coding exercises or challenges
Solve coding exercises using Dataflow and BigQuery
Debug and optimize your code

Answer questions and provide guidance in discussion forums

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Mentoring others will not only reinforce your understanding but also contribute to the learning community.

Browse courses on Mentoring

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Identify opportunities to provide assistance in discussion forums
Share your knowledge and expertise
Guide and support other learners

Build a Data Pipeline using Dataflow and BigQuery

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Creating a real-world data pipeline will provide you with hands-on experience and reinforce your understanding of the concepts covered in this course.

Browse courses on Data Pipeline

Show steps

Design your data pipeline
Implement your pipeline using Dataflow and BigQuery
Test and deploy your pipeline

Career center

Learners who complete ETL Processing on Google Cloud Using Dataflow and BigQuery will develop knowledge and skills that may be useful to these careers:

Data Engineer

Data engineers design the systems that manage and analyze large datasets. They are responsible for developing and maintaining the infrastructure that stores data, and for creating the tools that allow data scientists to access and analyze that data.

See salaries and explore the career path for Data Engineer

Data Analyst

Data analysts use their knowledge of data and statistics to help businesses make informed decisions. They analyze data to identify trends and patterns, and then develop recommendations for how to improve business outcomes.

See salaries and explore the career path for Data Analyst

Data Scientist

Data scientists use their knowledge of mathematics, statistics, and computer science to solve business problems. They develop and apply statistical models to data to predict outcomes and make recommendations for how to improve business outcomes.

See salaries and explore the career path for Data Scientist

Big Data Engineer

Big data engineers are responsible for designing and managing the infrastructure that stores and processes large datasets. They work with data scientists to develop and implement data processing pipelines, and they ensure that data is available and accessible to data analysts and scientists.

See salaries and explore the career path for Big Data Engineer

Business Intelligence Analyst

Business intelligence analysts use their knowledge of data and business to identify opportunities for improvement. They analyze data to identify trends and patterns, and then develop recommendations for how to improve business outcomes.

See salaries and explore the career path for Business Intelligence Analyst

Database Administrator

Database administrators are responsible for managing and maintaining databases. They ensure that data is stored securely and efficiently, and that it is available to users when they need it.

See salaries and explore the career path for Database Administrator

Cloud Architect

Cloud architects design and implement cloud computing solutions. They work with businesses to identify their cloud computing needs, and then design and implement solutions that meet those needs.

See salaries and explore the career path for Cloud Architect

DevOps Engineer

DevOps engineers work with developers and operations teams to ensure that software is developed and deployed efficiently. They help to automate the software development process, and they ensure that software is deployed securely and reliably.

See salaries and explore the career path for DevOps Engineer

Software Engineer

Software engineers design, develop, and maintain software applications. They work with businesses to identify their software needs, and then design and develop software that meets those needs.

See salaries and explore the career path for Software Engineer

Data Visualization Specialist

Data visualization specialists create visual representations of data. They use their knowledge of data and design to create visualizations that are both informative and engaging.

See salaries and explore the career path for Data Visualization Specialist

Information Security Analyst

Information security analysts protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction.

See salaries and explore the career path for Information Security Analyst

Network Engineer

Network engineers design, implement, and maintain computer networks. They work with businesses to identify their network needs, and then design and implement networks that meet those needs.

See salaries and explore the career path for Network Engineer

Quality Assurance Analyst

Quality assurance analysts test software to ensure that it meets quality standards. They work with developers and testers to identify and fix bugs, and they ensure that software is released on time and within budget.

See salaries and explore the career path for Quality Assurance Analyst

User Experience Designer

User experience designers design and develop user interfaces for websites, software applications, and other products. They work with businesses to identify their user needs, and then design and develop interfaces that are both user-friendly and aesthetically pleasing.

See salaries and explore the career path for User Experience Designer

Marketing Analyst

Marketing analysts use their knowledge of data and marketing to help businesses make informed decisions about their marketing campaigns. They analyze data to identify trends and patterns, and then develop recommendations for how to improve marketing outcomes.

See salaries and explore the career path for Marketing Analyst

Reading list

We've selected seven 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 ETL Processing on Google Cloud Using Dataflow and BigQuery.

Data Science on the Google Cloud Platform

Save

This comprehensive guide covers everything from the basics of BigQuery to advanced topics such as data modeling and machine learning.

Data Science on the Google Cloud Platform:...

Paperback

Data Science on the Google Cloud Platform

Kindle Edition

$$$

Hadoop: The Definitive Guide

Save

This classic book provides a comprehensive overview of Hadoop, including detailed explanations of its architecture, components, and tools.

Hadoop: The Definitive Guide: Storage and Analysis...

Paperback

Hadoop: The Definitive Guide

Kindle Edition

Hadoop: The Definitive Guide

Paperback

Hadoop: The Definitive Guide

Paperback

Spark: The Definitive Guide

Save

This comprehensive guide covers everything from the basics of Spark to advanced topics such as machine learning and graph processing.

Spark: The Definitive Guide

Paperback

Check price

Spark: The Definitive Guide

Kindle Edition

Check price

Data-Intensive Text Processing with MapReduce

Save

Provides a practical overview of how to use MapReduce to process large amounts of text data.

Data-Intensive Text Processing with MapReduce...

Paperback

Deep Learning with Python, Second Edition

Save

Provides a practical introduction to deep learning using Python, with a focus on building and training deep learning models.

Deep Learning with Python, Second Edition

Paperback

Deep Learning with Python, Second Edition

Kindle Edition

Python for Data Analysis

Save

Provides a practical introduction to data wrangling using Python, with a focus on using the Pandas library.

Python for Data Analysis: Data Wrangling with...

Paperback

Python for Data Analysis

Kindle Edition

Python for Data Analysis: Data Wrangling with...

Paperback

$$$

Python Data Science Handbook

Save

Provides a practical introduction to data visualization using Python and Matplotlib.

Python Data Science Handbook: Essential Tools for...

Paperback

$$$

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Effort

1 hour

Level

Intermediate

Via

Coursera

Institution

Google Cloud

Instructor

Google Cloud Training

Language

English

Good to know

Applies concepts in data engineering

Applies Apache Beam tool for data engineering

Uses Google Cloud Platform tools

Employs the BigQuery interface for database operations

Learners should have foundational knowledge in data engineering and working knowledge of cloud platforms, BigQuery, and Apache Beam

Course requires access to Google Cloud Platform and BigQuery, which can require payment to use

Share and help others discover this course.

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