This course is the first part of a 3-course series on Serverless Data Processing with Dataflow, offered by Google Cloud. It provides a solid foundation in Apache Beam, Dataflow, and the Beam Portability framework. Through a series of modules, learners will gain a thorough understanding of how to separate compute and storage with Dataflow, implement the right security model for their use case, and utilize identity, access, and management tools effectively.
The course is designed for individuals familiar with the Data Engineering specialization, and it is recommended to have prior knowledge from prerequisite courses covering core Dataflow principles and streaming basics concepts.
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The course starts with a brief introduction and refresher on Apache Beam and Dataflow, followed by in-depth modules on Beam Portability, separating compute and storage with Dataflow, IAM, quotas, permissions, security, and concludes with a summary and additional resources.
The Introduction module provides an overview of the course, including a refresher on Apache Beam, its relationship with Dataflow, and instructions on how to send feedback.
The Beam Portability module delves into the benefits of the Beam Portability framework, exploring topics such as Runner v2, container environments, and cross-language transforms.
The Separating Compute and Storage with Dataflow module covers essential aspects of Dataflow, including the Dataflow Shuffle Service, Dataflow Streaming Engine, and flexible resource scheduling.
The IAM, Quotas, and Permissions module provides insights into IAM, quotas, and permissions, enabling learners to effectively manage access and permissions for their Dataflow pipelines.
The Security module focuses on data locality, shared VPC, private IPs, and Customer-Managed Encryption Keys (CMEK). The lab at the end allows learners to set up IAM and networking for their Dataflow jobs.
The Summary module offers a concise recap of the course content, followed by additional resources for further learning and exploration.
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