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Google Cloud
This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery.
Read more
This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery.
Read more
This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery.
This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Redshift, you also learn about similarities and differences between Redshift and BigQuery to help you get started with data warehouses in BigQuery. After this course, you can continue your BigQuery journey by completing the skill badge quest titled: https://www.cloudskillsboost.google/quests/147">Build and Optimize Data Warehouses with BigQuery
This course is no longer available. Find something similar by browsing:
BigQuery
Redshift
SQL
Data Warehouse
Data Ingestion
Schema Design
Query Optimization
Register for this course and see more details by visiting:
OpenCourser.com/course/0m7yaq/bigquery
What's inside
Syllabus
BigQuery Architecture and Resource Provisioning
BigQuery Data Definition Model
BigQuery and Google Cloud IAM
BigQuery Data Ingestion
Read more
Syllabus
BigQuery Architecture and Resource Provisioning
BigQuery Data Definition Model
BigQuery and Google Cloud IAM
BigQuery Data Ingestion
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides a pathway for Redshift users to transition to BigQuery, enabling them to leverage their existing knowledge and skills
Delivers a solid foundation in BigQuery fundamentals, addressing key concepts and essential practices
Geared towards professionals with SQL-based cloud data warehouse experience, ensuring a seamless transition to BigQuery
Focuses on practical application through interactive lectures and hands-on labs, fostering a deeper understanding of BigQuery's capabilities
Facilitates a smooth learning experience by drawing parallels between Redshift and BigQuery, highlighting similarities and differences
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Reviews summary
Seamless bigquery transition for redshift
According to students, this course is an excellent resource for Redshift professionals transitioning to BigQuery. Learners consistently highlight the course's strength in providing direct comparisons between Redshift and BigQuery concepts, which is invaluable for bridging existing knowledge. The hands-on labs are frequently praised as practical and crucial for solidifying understanding. While the instructor's clarity and well-structured content receive high marks, some advanced users note that the course is foundational, wishing for deeper dives into complex optimization techniques. Minor technical glitches in labs were occasionally reported, but overall, it provides a strong foundation for BigQuery work.
Provides strong fundamentals, not a deep dive for experts.
"For someone with extensive Redshift experience, some parts felt a bit too fundamental."
"It's a fundamental course, so don't expect deep dives into every single feature, but it provides a very strong foundation."
"I was hoping for more advanced optimization techniques or complex data modeling scenarios specific to BigQuery."
Valuable insights into BigQuery performance tuning.
"I particularly appreciated the section on query optimization – very relevant for production workloads."
"The query optimization parts were particularly insightful."
"More depth on performance tuning would be good."
Well-structured content and clear instructor delivery.
"Excellent course! The content is well-structured and easy to follow. The instructor clearly knows their stuff."
"The instructor's clarity is commendable."
"The clarity of explanations and the hands-on practice were exceptional."
Essential for solidifying BigQuery understanding.
"The hands-on labs were practical and truly helped solidify my understanding."
"The practical exercises were great."
"The hands-on labs are crucial for learning and worked flawlessly."
"The hands-on coding and projects are the strongest part of the course for me."
Invaluable for transitioning from Redshift.
"This course was exactly what I needed to bridge the gap to BigQuery. The explanations comparing concepts were invaluable."
"It does a decent job of highlighting the differences from Redshift, which was helpful."
"The comparison to Redshift syntax and architecture was incredibly useful, saving me a lot of time."
"This course perfectly addressed my need to understand BigQuery from a Redshift perspective."
Some learners encountered minor technical hitches in labs.
"The labs were functional, though I encountered a couple of minor issues that required some troubleshooting on my end."
"I had some difficulty following the explanations, and the labs had issues loading data, which wasted time."
"Labs were generally fine but occasionally had minor setup quirks."
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 BigQuery Fundamentals for Redshift Professionals with these
activities:
Review SQL fundamentals
Show steps
Review and strengthen your foundational understanding of SQL, which is a prerequisite for success in this course.
Browse courses on
SQL
Show steps
-
Review primary SQL concepts
-
Practice writing and executing basic SQL queries
-
Complete online SQL tutorials or exercises
Review relational database concepts
Show steps
Revisit the key concepts associated with relational databases, including data modeling, normalization, and SQL syntax, to strengthen your foundation for BigQuery.
Browse courses on
Relational Database
Show steps
-
Review data modeling principles, such as entities, attributes, and relationships.
-
Go over normalization techniques to optimize database design.
-
Practice writing SQL queries to retrieve and manipulate data from a sample relational database.
Explore BigQuery documentation and tutorials
Show steps
Deepen your understanding of BigQuery's architecture, features, and best practices by exploring the comprehensive documentation and tutorials provided by Google.
Show steps
-
Review the BigQuery documentation for an overview of its architecture, data model, and query language.
-
Complete interactive tutorials to learn how to create datasets, tables, and perform queries using the BigQuery console or API.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice SQL queries
Show steps
Reinforce your understanding of SQL syntax and BigQuery-specific features through hands-on practice with guided exercises and problem-solving.
Browse courses on
SQL
Show steps
-
Complete a series of guided SQL exercises covering basic to advanced concepts.
-
Solve SQL puzzles and challenges to test your problem-solving skills.
-
Participate in online forums or communities to discuss SQL queries and exchange knowledge with others.
Join a BigQuery study group or online community
Show steps
Connect with other learners or industry professionals to discuss BigQuery concepts, share experiences, and collaborate on projects.
Show steps
-
Join an online forum or discussion group dedicated to BigQuery.
-
Participate in discussions, ask questions, and contribute your knowledge.
Create a blog post or article on BigQuery
Show steps
Consolidate your understanding of BigQuery by writing a blog post or article that shares your insights, experiences, or best practices with the wider community.
Show steps
-
Choose a specific topic related to BigQuery that you want to explore.
-
Research and gather information from various sources.
-
Write a well-structured and informative blog post or article.
Review SQL fundamentals
Show steps
Review and strengthen your foundational understanding of SQL, which is a prerequisite for success in this course.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Review primary SQL concepts
- Practice writing and executing basic SQL queries
- Complete online SQL tutorials or exercises
Review relational database concepts
Show steps
Revisit the key concepts associated with relational databases, including data modeling, normalization, and SQL syntax, to strengthen your foundation for BigQuery.
Browse courses on
Relational Database
Show steps
Show steps
Show steps
- Review data modeling principles, such as entities, attributes, and relationships.
- Go over normalization techniques to optimize database design.
- Practice writing SQL queries to retrieve and manipulate data from a sample relational database.
Explore BigQuery documentation and tutorials
Show steps
Deepen your understanding of BigQuery's architecture, features, and best practices by exploring the comprehensive documentation and tutorials provided by Google.
Show steps
Show steps
Show steps
- Review the BigQuery documentation for an overview of its architecture, data model, and query language.
- Complete interactive tutorials to learn how to create datasets, tables, and perform queries using the BigQuery console or API.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice SQL queries
Show steps
Reinforce your understanding of SQL syntax and BigQuery-specific features through hands-on practice with guided exercises and problem-solving.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Complete a series of guided SQL exercises covering basic to advanced concepts.
- Solve SQL puzzles and challenges to test your problem-solving skills.
- Participate in online forums or communities to discuss SQL queries and exchange knowledge with others.
Join a BigQuery study group or online community
Show steps
Connect with other learners or industry professionals to discuss BigQuery concepts, share experiences, and collaborate on projects.
Show steps
Show steps
Show steps
- Join an online forum or discussion group dedicated to BigQuery.
- Participate in discussions, ask questions, and contribute your knowledge.
Create a blog post or article on BigQuery
Show steps
Consolidate your understanding of BigQuery by writing a blog post or article that shares your insights, experiences, or best practices with the wider community.
Show steps
Show steps
Show steps
- Choose a specific topic related to BigQuery that you want to explore.
- Research and gather information from various sources.
- Write a well-structured and informative blog post or article.
Career center
Learners who complete BigQuery Fundamentals for Redshift Professionals will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst gathers, analyzes, interprets, and presents large datasets to help companies make informed decisions, implement better strategies, and improve overall business performance. This course may be useful for Data Analysts who want to learn how to use BigQuery to store and analyze large datasets.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data warehouses and data pipelines. This course may be useful for Data Engineers who want to learn how to use BigQuery to store and analyze large datasets.
Data Scientist
Data Scientist
A Data Scientist uses statistical and machine learning techniques to extract insights from data. This course may be useful for Data Scientists who want to learn how to use BigQuery to store and analyze large datasets.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data to identify trends and patterns that can help businesses make better decisions. This course may be useful for Business Intelligence Analysts who want to learn how to use BigQuery to store and analyze large datasets.
Data Architect
Data Architect
A Data Architect designs and builds data warehouses and data pipelines. This course may be useful for Data Architects who want to learn how to use BigQuery to store and analyze large datasets.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course may be useful for Database Administrators who want to learn how to use BigQuery to store and analyze large datasets.
Software Engineer
Software Engineer
A Software Engineer designs, builds, and maintains software applications. This course may be useful for Software Engineers who want to learn how to use BigQuery to store and analyze large datasets.
Cloud Engineer
Cloud Engineer
A Cloud Engineer designs, builds, and maintains cloud computing systems. This course may be useful for Cloud Engineers who want to learn how to use BigQuery to store and analyze large datasets.
Data Warehouse Manager
Data Warehouse Manager
A Data Warehouse Manager manages and maintains data warehouses. This course may be useful for Data Warehouse Managers who want to learn how to use BigQuery to store and analyze large datasets.
Information Security Analyst
Information Security Analyst
An Information Security Analyst protects computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. This course may be useful for Information Security Analysts who want to learn how to use BigQuery to store and analyze large datasets.
Big Data Architect
Big Data Architect
A Big Data Architect designs and builds big data systems. This course may be useful for Big Data Architects who want to learn how to use BigQuery to store and analyze large datasets.
Data Governance Analyst
Data Governance Analyst
A Data Governance Analyst develops and implements data governance policies and procedures. This course may be useful for Data Governance Analysts who want to learn how to use BigQuery to store and analyze large datasets.
Data Quality Analyst
Data Quality Analyst
A Data Quality Analyst ensures that data is accurate, complete, and consistent. This course may be useful for Data Quality Analysts who want to learn how to use BigQuery to store and analyze large datasets.
Data Privacy Analyst
Data Privacy Analyst
A Data Privacy Analyst develops and implements data privacy policies and procedures. This course may be useful for Data Privacy Analysts who want to learn how to use BigQuery to store and analyze large datasets.
Data Ethics Analyst
Data Ethics Analyst
A Data Ethics Analyst develops and implements data ethics policies and procedures. This course may be useful for Data Ethics Analysts who want to learn how to use BigQuery to store and analyze large datasets.
For more career information including salaries, visit:
OpenCourser.com/course/0m7yaq/bigquery
Reading list
We've selected six 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
BigQuery Fundamentals for Redshift Professionals.
Provides a comprehensive overview of data-intensive application design, covering topics such as data modeling, distributed systems, and performance optimization. Useful for those seeking to understand the underlying principles of data warehousing and analytics systems.
Provides a comprehensive overview of data engineering in Google Cloud, including BigQuery. It covers data integration, data processing, and analytics techniques, providing valuable insights for Redshift professionals who are looking to build end-to-end data pipelines in Google Cloud.
Provides a non-technical introduction to data analytics, covering concepts such as data collection, analysis, and visualization. Useful for those seeking to understand the broader context and applications of data warehousing and analytics.
Covers the Apache Hadoop framework, including its architecture, components, and ecosystem. While not directly related to BigQuery, it provides valuable insights into the broader landscape of big data processing and storage.
Covers the Apache Spark framework, including its architecture, APIs, and use cases. While not directly related to BigQuery, it provides valuable insights into big data processing and analytics techniques.
Covers Java-based big data analytics techniques, including data ingestion, processing, and analysis. While not specifically focused on BigQuery, it provides valuable insights into big data analytics programming.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0m7yaq/bigquery
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