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Building Batch Data Pipelines on Google Cloud

Google Cloud

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.

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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.

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.

This course is no longer available. Find something similar by browsing:
Cloud Data Fusion Serverless Data Processing Data Pipeline Architecture Google Dataproc BigQuery ETL Data Transformation

What's inside

Syllabus

Introduction
Introduction to Building Batch Data Pipelines
Executing Spark on Dataproc
Serverless Data Processing with Dataflow
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Covers multiple technologies for data transformation in the Google Cloud ecosystem
Provides in-depth knowledge of Extract, Load and Transform (ELT) and Extract, Transform and Load (ETL) paradigms for data pipelines
Taught by Google Cloud, an expert in data analytics and infrastructure
Suitable for learners interested in data engineering, data analytics, and cloud computing
Offers hands-on experience through Qwiklabs, allowing learners to apply what they learn
Covers BigQuery, Spark on Dataproc, Cloud Data Fusion, and Dataflow for data transformation

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Reviews summary

Practical gcp batch data pipeline fundamentals

According to learners, this course offers a largely positive experience for those looking to build data pipelines on Google Cloud. Students consistently praise the invaluable hands-on labs and Qwiklabs integration, which make complex topics like Dataflow and Dataproc more accessible. Many found the explanations clear and concepts well-reinforced, particularly regarding ELT/ETL paradigms and the Dataflow service, providing a solid foundational understanding. However, some indicate the course may offer insufficient depth for advanced users or real-world optimization, noting that specific modules like Cloud Data Fusion and Dataproc could benefit from more detailed coverage or updated content.
Provides a strong basis for core GCP data pipeline concepts.
"A solid introduction to building batch pipelines on GCP. The content on BigQuery and Dataflow was clear."
"I came in with some basic GCP knowledge, and this course significantly boosted my understanding of batch processing. The coverage of different paradigms was also very insightful."
"I found the initial modules on EL, ELT, ETL concepts very helpful for setting the stage."
Practical Qwiklabs are highly effective for learning.
"The Qwiklabs are incredibly helpful and hands-on, making complex topics like Dataflow and Dataproc much easier to grasp. Highly recommend."
"Excellent course! The explanations are clear and the hands-on labs using Qwiklabs are invaluable."
"The Qwiklabs environment truly makes learning practical and memorable. I now feel confident building my first data pipelines on GCP."
Some modules could benefit from more depth or updates.
"My only minor gripe is that Cloud Data Fusion felt a bit rushed, and I wish there were more advanced optimization techniques covered."
"The course has good information, but some sections, particularly on Dataproc, felt a bit outdated or could have used more depth."
"I felt the section on Cloud Composer could have been expanded, it's a critical tool for orchestration."
May lack depth for experienced users or advanced topics.
"Honestly, I found this course a bit basic. If you already have experience with Spark or Dataflow, you might not get much new information."
"The course is a good starting point, but not comprehensive for real-world scenarios or advanced optimization techniques."
"While the course provides a good overview, the depth of coverage for some services felt insufficient... requires additional self-study."

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 pipeline paradigms
Review Extract and Load (EL), Extract, Load and Transform (ELT) and Extract, Transform and Load (ETL) paradigms to strengthen your understanding of batch data processing.
Show steps
  • Review course materials on pipeline paradigms
  • Research EL, ELT, and ETL online
  • Create a table comparing the features and benefits of each paradigm
Review Data Pipeline Concepts
Solidify your knowledge of data pipeline architectures and terminology commonly used in the industry.
Browse courses on Data Pipelines
Show steps
  • Summarize Key Concepts of EL, ELT, and ETL
  • Explain Data Pipelines Architectures
  • Describe Data Pipeline Lifecycle
Execute Spark on Dataproc
Enhance your data pipeline skills by following guided tutorials on how to execute Spark on Dataproc, a managed Spark cluster service on Google Cloud.
Browse courses on Dataproc
Show steps
  • Find tutorials on executing Spark on Dataproc
  • Follow the steps in the tutorials
  • Experiment with different Spark configurations
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Collaborate with Peers on Data Pipeline Techniques
Exchange ideas, discuss challenges, and share best practices with other learners to enhance your understanding of data pipeline concepts.
Browse courses on Data Pipelines
Show steps
  • Join a study group or online forum
  • Contribute to discussions and ask questions
  • Organize a virtual workshop or presentation
Explore Google Cloud Services for Data Pipelines
Gain hands-on experience with tools and technologies crucial for data pipelines on Google Cloud Platform.
Browse courses on Google Cloud Services
Show steps
  • Follow tutorials on Building Data Pipelines with BigQuery
  • Complete Dataproc Spark Quickstart
  • Deploy a Data Pipeline using Cloud Data Fusion
Serverless data processing with Dataflow
Develop proficiency in serverless data processing by completing repetitive exercises and drills on Dataflow, Google Cloud's fully managed stream processing service.
Browse courses on Dataflow
Show steps
  • Solve Dataflow coding challenges
  • Build small-scale Dataflow pipelines
  • Analyze the performance of your pipelines
Attend a workshop on Cloud Data Fusion
Sharpen your data pipeline expertise by attending a workshop on Cloud Data Fusion, Google Cloud's fully managed, no-code data integration service.
Browse courses on Cloud Data Fusion
Show steps
  • Find a Cloud Data Fusion workshop
  • Register for the workshop
  • Attend the workshop and actively participate
Practice Data Transformation with Spark on Dataproc
Enhance your proficiency in transforming and processing data with Spark by completing exercises and challenges.
Browse courses on Data Transformation
Show steps
  • Solve Spark Data Manipulation Exercises
  • Practice Data Aggregation and Grouping
  • Implement Spark Data Quality Checks
Build a Data Pipeline Project on Google Cloud
Integrate your knowledge and skills by developing a practical data pipeline project from scratch using Google Cloud technologies.
Browse courses on Cloud Platform
Show steps
  • Identify a data source and define the data pipeline scope
  • Design the pipeline architecture and select appropriate services
  • Implement data ingestion, transformation, and visualization components
Participate in Data Pipeline Challenges
Challenge yourself against peers and industry experts by participating in data pipeline hackathons or competitions.
Show steps
  • Monitor data science and analytics competitions
  • Form a team or participate individually
  • Design and implement a solution to a real-world data pipeline problem
Mentor a junior data engineer
Enhance your knowledge and leadership skills by mentoring a junior data engineer, helping them develop their data pipeline skills and grow their career.
Browse courses on Mentoring
Show steps
  • Find a junior data engineer to mentor
  • Set up regular mentoring sessions
  • Provide guidance on data pipeline design and implementation
  • Review their code and offer constructive feedback
Support Fellow Learners in Data Pipeline Projects
Reinforce your skills and contribute to the community by mentoring other learners in data pipeline projects.
Browse courses on Mentoring
Show steps
  • Contribute actively to online forums and discussion groups
  • Organize study sessions or workshops
  • Provide feedback and support to other learners
Contribute to Open Source Data Pipeline Projects
Gain practical experience and contribute to the data pipeline ecosystem by participating in open source projects.
Browse courses on Data Pipelines
Show steps
  • Identify open source projects relevant to data pipelines
  • Review code, report issues, and suggest improvements
  • Contribute to documentation and tutorials

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 Integration Architect
Data Integration Architects design and implement data integration solutions to connect disparate data sources and enable seamless data exchange. This course can provide a Data Integration Architect with in-depth knowledge of data pipeline technologies on Google Cloud, enabling them to design and implement scalable and interoperable data integration solutions.
See salaries and explore the career path for Data Integration Architect
Data Engineer
A Data Engineer designs, builds, deploys, and maintains data pipelines for various purposes within an organization. This course can provide a Data Engineer with hands-on experience using Google Cloud technologies for data transformation and pipeline management, enhancing their skillset for building scalable and efficient data ingestion and processing systems.
See salaries and explore the career path for Data Engineer
Data Architect
Data Architects design and manage the overall data architecture of an organization, including data pipelines for data ingestion, transformation, and storage. This course can provide a Data Architect with hands-on experience in building data pipelines on Google Cloud, enabling them to make informed decisions about data pipeline design and implementation within the broader data architecture.
See salaries and explore the career path for Data Architect
Machine Learning Engineer
Machine Learning Engineers leverage data pipelines to automate the data acquisition, cleaning, and transformation processes necessary for training and deploying machine learning models. This course can provide a Machine Learning Engineer with hands-on experience in building scalable data pipelines on Google Cloud using various technologies, helping them to streamline their model development and deployment workflows.
See salaries and explore the career path for Machine Learning Engineer
Research Scientist
Research Scientists working with large datasets can benefit from this course by gaining hands-on experience in building data pipelines on Google Cloud. This course can help Research Scientists develop a strong foundation in data pipeline design and implementation, enabling them to efficiently manage and process data for their research projects.
See salaries and explore the career path for Research Scientist
Software Engineer
Software Engineers specializing in data engineering or cloud computing can benefit from this course by gaining hands-on experience in building data pipelines on Google Cloud. This course can help Software Engineers develop a strong foundation in data pipeline design and implementation, enabling them to contribute effectively to data-driven projects and initiatives.
See salaries and explore the career path for Software Engineer
Database Administrator
Database Administrators are responsible for managing and maintaining databases, including data pipelines used for data ingestion, transformation, and storage. This course may be useful for a Database Administrator seeking to enhance their understanding of data pipeline design and implementation, enabling them to optimize data management processes and ensure data integrity.
See salaries and explore the career path for Database Administrator
Data Analyst
A Data Analyst can use their knowledge of building data pipelines to simplify the extraction, transformation, and loading of data from various sources, enabling them to perform insightful analysis for organizations seeking to make data-driven decisions. This course may help a Data Analyst build a foundation in data pipeline design and execution to support their analytical work.
See salaries and explore the career path for Data Analyst
Data Consultant
Data Consultants advise organizations on data management and analytics strategies, including data pipeline design and implementation. This course may be useful for a Data Consultant seeking to enhance their understanding of data pipeline technologies on Google Cloud, enabling them to provide informed recommendations and guidance to their clients.
See salaries and explore the career path for Data Consultant
Data Governance Specialist
Data Governance Specialists develop and implement policies and procedures to ensure the integrity, security, and privacy of data within an organization, including data pipelines. This course may be useful for a Data Governance Specialist seeking to gain a deeper understanding of data pipeline technologies and best practices, enabling them to effectively monitor and govern data pipelines.
See salaries and explore the career path for Data Governance Specialist
Cloud Engineer
Cloud Engineers are responsible for designing, building, and managing cloud-based infrastructure and services. This course may be helpful for a Cloud Engineer seeking to specialize in data engineering or to gain a deeper understanding of data pipeline technologies on Google Cloud, enabling them to effectively provision and manage data pipelines within cloud environments.
See salaries and explore the career path for Cloud Engineer
Technical Lead
Technical Leads guide and manage technical teams, including teams responsible for data pipeline development and maintenance. This course may be helpful for a Technical Lead seeking to gain a deeper understanding of data pipeline technologies and best practices, enabling them to effectively lead and support their teams in building and managing scalable and efficient data pipelines.
See salaries and explore the career path for Technical Lead
Product Manager
Product Managers responsible for data-driven products or features can benefit from this course by gaining a deeper understanding of data pipeline technologies and their impact on product development. This course may help Product Managers make informed decisions about data pipeline design and implementation, enabling them to build products that effectively leverage data for better outcomes.
See salaries and explore the career path for Product Manager
Business Analyst
Business Analysts work with stakeholders to understand business requirements and translate them into technical specifications, including those related to data pipelines. This course may be helpful for a Business Analyst seeking to gain a deeper understanding of data pipeline technologies and best practices, enabling them to effectively bridge the gap between business needs and technical solutions.
See salaries and explore the career path for Business Analyst
Data Scientist
Data Scientists often rely on data pipelines to access and process large amounts of data for their modeling and analysis tasks. This course may be useful for a Data Scientist seeking to gain practical experience in building data pipelines on Google Cloud, enabling them to efficiently prepare and transform data for their machine learning and statistical modeling projects.
See salaries and explore the career path for Data Scientist

Reading list

We've selected three 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.
Cover image
Kafka: The Definitive Guide
Save
Kafka popular distributed messaging system for building real-time data pipelines. provides a comprehensive guide to Kafka's architecture, features, and use cases, offering valuable insights for practitioners looking to incorporate Kafka into their data pipelines.
Kafka: The Definitive Guide: Real-Time Data and...
Paperback
$$
Cover image
Generative AI on AWS
Save
Focuses on building data pipelines using AWS Glue, a fully managed data integration service. It provides a practical guide to designing, implementing, and managing data pipelines on AWS, offering valuable insights for practitioners working with AWS.
Generative AI on AWS: Building Context-Aware...
Paperback
$$$
(Español) IA generativa en AWS: Creación de aplicaciones...
Paperback
$$
Generative AI on AWS
Kindle Edition
$$
Cover image
Hadoop: The Definitive Guide
Save
Hadoop foundational technology for big data processing, and this book provides a comprehensive overview of its architecture, components, and use cases. While not specifically focused on data pipelines, it offers valuable background knowledge for practitioners working with data pipelines on Hadoop.
Hadoop: The Definitive Guide: Storage and Analysis...
Paperback
$$
Hadoop: The Definitive Guide
Kindle Edition
$$
Hadoop: The Definitive Guide
Paperback
$$
Hadoop: The Definitive Guide
Paperback
$$

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Time
135 hours
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Intermediate
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Instructor
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Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Covers multiple technologies for data transformation in the Google Cloud ecosystem
Provides in-depth knowledge of Extract, Load and Transform (ELT) and Extract, Transform and Load (ETL) paradigms for data pipelines
Taught by Google Cloud, an expert in data analytics and infrastructure
Suitable for learners interested in data engineering, data analytics, and cloud computing
Offers hands-on experience through Qwiklabs, allowing learners to apply what they learn
Covers BigQuery, Spark on Dataproc, Cloud Data Fusion, and Dataflow for data transformation
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