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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.

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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

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What's inside

Syllabus

BigQuery Architecture and Resource Provisioning
BigQuery Data Definition Model
BigQuery and Google Cloud IAM
BigQuery Data Ingestion
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BigQuery Schema Design and Optimization
SQL in BigQuery
Course Resources

Good to know

Know what's good
, what to watch for
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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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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
Review and strengthen your foundational understanding of SQL, which is a prerequisite for success in this course.
Browse courses on SQL
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  • Review primary SQL concepts
  • Practice writing and executing basic SQL queries
  • Complete online SQL tutorials or exercises
Review relational database concepts
Revisit the key concepts associated with relational databases, including data modeling, normalization, and SQL syntax, to strengthen your foundation for BigQuery.
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  • 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
Deepen your understanding of BigQuery's architecture, features, and best practices by exploring the comprehensive documentation and tutorials provided by Google.
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  • 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
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Practice SQL queries
Reinforce your understanding of SQL syntax and BigQuery-specific features through hands-on practice with guided exercises and problem-solving.
Browse courses on SQL
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  • 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
Connect with other learners or industry professionals to discuss BigQuery concepts, share experiences, and collaborate on projects.
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  • 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
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.

Career center

Learners who complete BigQuery Fundamentals for Redshift Professionals will develop knowledge and skills that may be useful to these careers:
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.
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.
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 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.
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 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.
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 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.
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
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.
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.
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
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.
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 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.

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.

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