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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
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
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
Show steps
  • 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
Familiarizing yourself with BigQuery documentation will provide you with a solid foundation for using the platform in this course.
Show steps
  • 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
Engaging in discussion forums will allow you to connect with other learners, share knowledge, and seek clarification on course-related topics.
Show steps
  • 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
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
Mentoring others will not only reinforce your understanding but also contribute to the learning community.
Browse courses on Mentoring
Show steps
  • 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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Information Security Analyst
Information security analysts protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction.
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.
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.
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.
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.

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.
This comprehensive guide covers everything from the basics of BigQuery to advanced topics such as data modeling and machine learning.
This comprehensive guide covers everything from the basics of Spark to advanced topics such as machine learning and graph processing.
Provides a practical overview of how to use MapReduce to process large amounts of text data.
Provides a practical introduction to deep learning using Python, with a focus on building and training deep learning models.
Provides a practical introduction to data visualization using Python and Matplotlib.

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