Serverless Data Processing with Dataflow: Foundations
Serverless Data Processing with Dataflow: Foundations
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
Course Info
Objectives
- Demonstrate how Apache Beam and Dataflow work together to fulfill your organization’s data processing needs
- Summarize the benefits of the Beam Portability Framework and enable it for your Dataflow pipelines
- Enable Shuffle & Streaming Engine for batch & streaming pipelines respectively for maximum performance
- Enable Flexible Resource Scheduling for more cost efficient performance
- Select the right combination of IAM permissions for your Dataflow job
- Implement best practices for a secure data processing environment
Prerequisites
Completed the following on-demand courses:
1) Building Batch Data Pipelines on Google Cloud
2) Building Resilient Streaming Analytics Systems on Google Cloud
Audience
Data engineers, data analysts and data scientists aspiring to develop Data Engineering skills.
Available languages
English, español (Latinoamérica), 日本語, and português (Brasil)
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