This comprehensive course by Google Cloud empowers Snowflake professionals to seamlessly transition to working with BigQuery, a leading cloud data warehouse. Through interactive lectures and hands-on labs, participants learn the fundamentals of BigQuery, including resource provisioning, data asset creation, secure data sharing, data ingestion, and query optimization. Drawing upon their existing knowledge of Snowflake, professionals gain insights into the similarities and differences between Snowflake and BigQuery, facilitating a smooth transition to the latter. The course is designed to provide a deep understanding of BigQuery’s architecture and data definition model, enabling professionals to implement best practices for schema design, data ingestion, and query optimization. Additionally, participants will explore BigQuery and Google Cloud IAM, gaining valuable knowledge on securing and managing access to resources within BigQuery.
Through this course, participants will acquire the necessary skills to describe BigQuery’s architecture, provision resources efficiently, and implement common patterns and best practices for designing schemas, ingesting data, and querying data in BigQuery. The course also facilitates a comprehensive comparison between Snowflake and BigQuery, allowing professionals to identify the differences and commonalities between the two platforms. Upon completion of the course, professionals will be equipped to work confidently with BigQuery, leveraging their existing expertise in SQL-based cloud data warehouses to excel in this new environment.
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This course comprises modules on BigQuery architecture and resource provisioning, data definition model, Google Cloud IAM, data ingestion, schema design, optimization, and SQL in BigQuery.
This module delves into the architecture and resource provisioning of BigQuery. Participants will gain insights into the basics of Google Cloud Platform and Qwiklabs, followed by an in-depth understanding of BigQuery’s architecture and resource provisioning. The module also includes a lab session on monitoring BigQuery workloads, providing hands-on experience in managing and optimizing BigQuery performance.
This module focuses on the data definition model in BigQuery, equipping participants with the knowledge to create, secure, and share data assets using best practices. Through interactive content and hands-on labs, participants will gain a comprehensive understanding of the data definition model in BigQuery, enabling them to implement effective strategies for managing and securing data assets within the platform.
In this module, participants will explore the integration of BigQuery with Google Cloud IAM, learning how to effectively manage access to resources and secure data within BigQuery. The module provides valuable insights into implementing best practices for securing and managing access to resources, ensuring a robust security framework within BigQuery.
This module focuses on BigQuery data ingestion, providing participants with the skills to implement common patterns and best practices for ingesting data into BigQuery. Through interactive lectures and hands-on labs, participants will gain practical experience in efficiently ingesting data into BigQuery, ensuring seamless data integration within the platform.
Participants will gain expertise in schema design and optimization within BigQuery through this module. The module covers best practices for designing schemas, optimizing query performance, and implementing efficient data models in BigQuery. Through interactive content and hands-on labs, participants will acquire the necessary skills to design and optimize schemas for enhanced performance and data management within BigQuery.
This module focuses on SQL in BigQuery, providing participants with the essential skills to query data effectively within the platform. Participants will gain practical experience in writing and executing SQL queries in BigQuery, enabling them to harness the full potential of the platform for data analysis and insights.
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