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Learn how to use Google BigQuery to enhance your data engineering and machine learning skills in this practical, instructor-led course. Taught by experienced cloud architect and author Dan Sullivan, you’ll work with BigQuery’s serverless architecture, advanced SQL, and data warehousing features to efficiently manage and analyze large datasets.
Read more
Learn how to use Google BigQuery to enhance your data engineering and machine learning skills in this practical, instructor-led course. Taught by experienced cloud architect and author Dan Sullivan, you’ll work with BigQuery’s serverless architecture, advanced SQL, and data warehousing features to efficiently manage and analyze large datasets.
Read more
Learn how to use Google BigQuery to enhance your data engineering and machine learning skills in this practical, instructor-led course. Taught by experienced cloud architect and author Dan Sullivan, you’ll work with BigQuery’s serverless architecture, advanced SQL, and data warehousing features to efficiently manage and analyze large datasets.
This course is suitable for both beginners and those with experience. You’ll get hands-on practice with data ingestion, transformation, and building reliable data pipelines. The curriculum covers how to create, evaluate, and deploy machine learning models within BigQuery, as well as recent generative AI applications.
Through real-world projects and clear instruction, you’ll build the skills needed to use BigQuery in your day-to-day work. Whether you’re new to the field or looking to expand your knowledge, this course offers practical tools and techniques for data engineering and machine learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/4g86hv/google
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Career center
Learners who complete Google BigQuery for Data and ML Engineers will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer plays a pivotal role in designing, building, and maintaining the infrastructure for data generation and processing. This course, "Google BigQuery for Data and ML Engineers," is an exceptional foundation for aspiring and current Data Engineers. It directly addresses the core competencies of data ingestion, transformation, and constructing reliable data pipelines, all within the robust BigQuery ecosystem. Learners will gain hands-on practice with BigQuery's serverless architecture and advanced SQL, essential for managing and analyzing large datasets efficiently. For those looking to excel in building scalable data solutions on Google Cloud, this course provides the practical tools and techniques needed to succeed in day-to-day data engineering tasks.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you develop and deploy machine learning models, moving them from research to production. This course is specifically designed to equip you with critical skills for this specialized field. It delves into machine learning workflows, covering classification, regression, and time series modeling directly within BigQuery. Furthermore, you will gain practical experience with creating, evaluating, and deploying these models, as well as exploring recent generative AI applications. The "Google BigQuery for Data and ML Engineers" course helps build the expertise required to leverage BigQuery's capabilities for powerful and scalable machine learning solutions, a highly sought-after skill in today's tech landscape.
Big Data Engineer
Big Data Engineer
A Big Data Engineer builds and maintains scalable systems for processing and analyzing vast volumes of data. The "Google BigQuery for Data and ML Engineers" course is an ideal fit for this role, as BigQuery is a premier big data warehousing solution. This course provides extensive hands-on practice with data ingestion (batch and streaming), transformation, and building reliable data pipelines, all essential skills for a Big Data Engineer. Learners will gain a deep understanding of BigQuery's serverless architecture and advanced SQL to efficiently manage and explore large datasets, preparing them to tackle the complexities of large-scale data processing in the real world.
Cloud Data Architect
Cloud Data Architect
A Cloud Data Architect designs and oversees an organization's data strategy in cloud environments, ensuring scalability, security, and efficiency. This course, taught by an experienced cloud architect, offers invaluable insights into BigQuery's serverless architecture and data warehousing features. Understanding how to manage and analyze large datasets, implement data ingestion strategies, and build data pipelines are fundamental to designing robust cloud data solutions. For those aspiring to become a Cloud Data Architect, this course provides a deep understanding of a leading cloud data platform, helping to sculpt the foundational knowledge for developing comprehensive and high-performing data architectures within Google Cloud.
Analytics Engineer
Analytics Engineer
Analytics Engineers bridge the gap between data engineering and data analysis, focusing on building reliable and accessible data models for decision-making. The "Google BigQuery for Data and ML Engineers" course directly addresses many core responsibilities of an Analytics Engineer. It covers data ingestion, transformation, and the creation of dependable data pipelines, all utilizing BigQuery's powerful serverless architecture and advanced SQL capabilities. For an Analytics Engineer, understanding how to efficiently manage and analyze large datasets is crucial. This course provides practical experience with data quality checks and data exploration, helping to ensure the integrity and usability of data assets for downstream analytics.
Data Scientist
Data Scientist
A Data Scientist analyzes complex data to extract insights, build predictive models, and support strategic decision-making. This course is particularly helpful for this career path, as it deeply explores machine learning workflows, including classification, regression, and time series modeling. Proficiency in advanced SQL and the ability to manage and explore large datasets, as covered in this course, are crucial for any Data Scientist. While the role often involves broader statistical and programming knowledge, the focus on practical applications of generative AI and text processing within BigQuery provides a strong foundation for analytical and modeling tasks. Many Data Scientist roles often require or benefit from an advanced degree.
AI Engineer
AI Engineer
An AI Engineer develops, implements, and maintains artificial intelligence systems and applications. This course is highly relevant for an AI Engineer, as it comprehensively covers the creation, evaluation, and deployment of machine learning models directly within BigQuery. Crucially, the "Google BigQuery for Data and ML Engineers" curriculum also delves into recent generative AI applications and text processing, providing practical skills for building modern AI solutions. For an AI Engineer focused on leveraging powerful cloud platforms for scalable AI model development and deployment, this course offers the hands-on experience and conceptual understanding needed to succeed in this rapidly evolving field.
Business Intelligence Developer
Business Intelligence Developer
A Business Intelligence Developer designs and implements solutions that transform raw data into actionable insights for business users. The "Google BigQuery for Data and ML Engineers" course equips learners with directly transferable skills for this role. You will gain hands-on practice with data ingestion, transformation, and applying advanced SQL for efficient data analysis, all within BigQuery's data warehousing features. These capabilities are fundamental for building robust reporting dashboards and analytical tools. This course offers practical tools and techniques specifically for managing and analyzing large datasets, which are essential for any Business Intelligence Developer seeking to build scalable and reliable BI solutions.
Solutions Architect
Solutions Architect
A Solutions Architect designs comprehensive technical solutions for an organization, often integrating various technologies. Understanding modern data platforms is paramount for this role. The "Google BigQuery for Data and ML Engineers" course provides deep insight into BigQuery's serverless architecture, data engineering capabilities like ingestion and pipelines, and its machine learning functionalities. For a Solutions Architect, comprehending how to leverage BigQuery for managing and analyzing large datasets, and for deploying generative AI applications, is critical for designing scalable and innovative data-driven solutions on Google Cloud. This course helps build a robust understanding of a key component in many modern cloud architectures.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst applies mathematical and statistical methods to financial and risk management problems, often requiring advanced data analysis. This course is useful for a Quantitative Analyst due to its focus on managing and analyzing large datasets using advanced SQL. The curriculum's exploration of machine learning workflows, including regression and time series modeling directly in BigQuery, provides relevant skills for building analytical models. While Quantitative Analyst roles often require advanced degrees in fields like mathematics, statistics, or finance, the practical data manipulation and modeling skills from this course can significantly enhance one's ability to work with vast financial datasets.
Data Product Manager
Data Product Manager
A Data Product Manager defines the strategy, roadmap, and features for data-driven products. While not a hands-on technical role, a strong understanding of underlying data technologies is crucial. The "Google BigQuery for Data and ML Engineers" course helps build this understanding by covering BigQuery's serverless architecture, data warehousing, and the challenges of managing large datasets. Knowing how data ingestion, transformation, and reliable data pipelines are built, and understanding the scope of machine learning model deployment and generative AI applications within BigQuery, is invaluable for effectively communicating with engineering teams and making informed product decisions for a Data Product Manager.
Database Administrator
Database Administrator
A Database Administrator typically manages and maintains database systems, ensuring their performance, security, and availability. While BigQuery's serverless nature abstracts away some traditional DBA tasks, the "Google BigQuery for Data and ML Engineers" course may be useful for a Database Administrator. It covers comprehensive principles of data warehousing, efficient management of large datasets, and data quality checks, which are vital for any data storage system. Understanding BigQuery's architecture and how data is ingested and explored using advanced SQL provides valuable context for a DBA operating in a modern cloud environment, even if the hands-on administration differs from traditional relational databases.
Data Governance Specialist
Data Governance Specialist
A Data Governance Specialist develops and implements policies and procedures for data quality, privacy, and compliance. The "Google BigQuery for Data and ML Engineers" course may be useful for a Data Governance Specialist by providing insights into how data is managed within a modern cloud data warehouse. Understanding BigQuery's architecture, data ingestion processes, and data quality checks is fundamental to establishing effective governance frameworks. Knowing how large datasets are handled and transformed helps a Data Governance Specialist ensure adherence to standards from the ground up, identifying potential risks and implementing controls throughout the data lifecycle within a BigQuery environment.
Technical Trainer
Technical Trainer
A Technical Trainer designs and delivers educational programs on specific technologies or software. The "Google BigQuery for Data and ML Engineers" course may be useful for a Technical Trainer specializing in cloud data platforms or machine learning. The comprehensive overview of BigQuery's architecture, data engineering capabilities (ingestion, transformation, pipelines), and machine learning workflows provides a robust foundation for developing and teaching advanced courses. Understanding how to work with serverless architecture, advanced SQL, and practical generative AI applications, as covered in this course, allows a Technical Trainer to provide relevant, in-depth instruction to learners seeking to master BigQuery.
Site Reliability Engineer
Site Reliability Engineer
A Site Reliability Engineer ensures the reliability, performance, and availability of large-scale systems. While not directly an SRE course, "Google BigQuery for Data and ML Engineers" may be useful for a Site Reliability Engineer who supports data infrastructure built on BigQuery. Understanding BigQuery's serverless architecture, how data pipelines are built and managed, and the performance characteristics of large dataset analysis is crucial for monitoring, troubleshooting, and optimizing data systems. For an SRE working with data-intensive applications or ML platforms, knowledge of BigQuery's operational aspects, as illuminated by this course's content, can significantly enhance their ability to maintain system health.
For more career information including salaries, visit:
OpenCourser.com/course/4g86hv/google
Reading list
We haven't picked any books for this reading list yet.
This study guide for the Google Cloud Professional Data Engineer certification includes substantial content on BigQuery, as it's a key component of the exam. It's a useful resource for those preparing for certification and provides a structured approach to understanding BigQuery's features and best practices.
While not solely focused on BigQuery, this book provides essential context by demonstrating how BigQuery fits into the larger Google Cloud data science ecosystem. It's excellent for understanding data pipelines and leveraging BigQuery for analytical tasks. is suitable for those looking to see BigQuery's application in real-world data science scenarios.
Good choice for beginners who want to learn how to use BigQuery for data analysis. It covers the basics of SQL, as well as how to use BigQuery's built-in data analysis functions.
This is the essential reference for anyone working with BigQuery. It provides comprehensive coverage of all aspects of the service, making it suitable for gaining both a broad understanding and deep expertise. It must-have for professionals and advanced learners.
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Another strong contender for learning SQL, this book offers a solid foundation for interacting with databases like BigQuery. It's suitable for beginners and helps solidify understanding of query writing, a core skill for BigQuery users. is more valuable as foundational reading.
Provides a broader view of the Google Cloud Platform, placing BigQuery within the context of other GCP services. It's helpful for understanding how BigQuery integrates with tools for computing, storage, and machine learning, enhancing the breadth of knowledge for users.
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While focused on general database programming, this book highlights common SQL mistakes and inefficient patterns. Understanding these antipatterns is valuable for writing optimized and maintainable queries in BigQuery. It's more suited for those with some SQL experience looking to improve their coding practices.
A foundational text in data warehousing, this book introduces dimensional modeling, a crucial concept for designing efficient data structures in BigQuery. While not BigQuery-specific, it provides essential prerequisite knowledge for anyone working with data warehouses, including those on cloud platforms. It's a classic and highly recommended for a deep understanding of data modeling principles.
Provides a comprehensive methodology for designing, developing, and deploying data warehouses. While not specific to BigQuery, the principles and techniques discussed are highly relevant to building effective data warehousing solutions on the platform. It's a classic in the field and valuable for understanding the broader context of data warehousing projects.
Considered a foundational text in data warehousing, Inmon's book presents the enterprise data warehouse (EDW) approach. Understanding this methodology provides valuable background knowledge for appreciating how BigQuery fits into modern data architectures. It's a classic that offers a different perspective compared to the Kimball approach.
For those looking to master advanced SQL concepts and techniques applicable to BigQuery, this book is an excellent resource. It delves into complex querying scenarios and optimization, making it suitable for experienced SQL users and professionals aiming to write highly efficient BigQuery queries.
Focuses on data engineering on Google Cloud, with significant sections dedicated to using BigQuery within data pipelines. It's highly relevant for understanding how to ingest, transform, and load data into BigQuery, a key aspect of working with the platform. This is valuable for data engineers and those interested in the operational aspects of BigQuery.
Provides a high-level, accessible introduction to data warehousing concepts. It's a good starting point for high school and early undergraduate students to grasp the basic ideas behind data warehousing before diving into a specific platform like BigQuery.
Focusing specifically on star schema design, a common modeling technique used in data warehouses including BigQuery, this book offers in-depth knowledge on this crucial topic. It's valuable for those looking to optimize their data models in BigQuery for performance and usability.
While not directly about BigQuery, this book provides a foundational understanding of the principles behind modern data systems, including distributed databases and data processing. This knowledge is highly beneficial for understanding BigQuery's underlying architecture and capabilities at a deeper level.
This guide offers a broad introduction to the Google Cloud Platform, including its big data services like BigQuery. It's useful for beginners to understand the ecosystem in which BigQuery operates and how it fits into a larger cloud strategy. It's a good starting point before diving into BigQuery-specific details.
Provides a practical approach to building data and AI solutions on Google Cloud, with significant coverage of BigQuery's role in these solutions. It helps in understanding contemporary uses of BigQuery in enterprise settings and is valuable for professionals and graduate students.
Provides a practical guide to using Pandas for data analysis. It covers all aspects of Pandas, from data loading and cleaning to data manipulation and visualization.
Provides a practical guide to building and managing data science teams. It covers topics such as hiring, training, and motivating data scientists, as well as best practices for data science project management.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4g86hv/google
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Effort
Less than 1 week
Level
Intermediate
Via
Coursera
Institution
Pearson
Instructor
Pearson
Session
October 27, 2025
- November 10, 2025
Language
English
Transcript
English
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