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DataCamp

Serverless Data Processing with Dataflow: Foundations

via DataCamp

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Overview

This course covers the foundations of serverless data processing with Apache Beam and Dataflow.

This course covers the foundations of serverless data processing with Apache Beam and Dataflow. Topics include Beam portability, Runner v2, container environments, cross-language transforms, Dataflow Shuffle and Streaming Engine, Flexible Resource Scheduling, IAM, quotas, networking, and security with CMEK.

Syllabus

  • Introduction
    • This module covers the course outline and does a quick refresh on the Apache Beam programming model and Google’s Dataflow managed service.
  • Beam Portability
    • In this module we are going to learn about four sections, Beam Portablity, Runner v2, Container Environments, and Cross-Language Transforms.
  • Separating Compute and Storage with Dataflow
    • In this module we discuss how to separate compute and storage with Dataflow. This module contains four sections Dataflow, Dataflow Shuffle Service, Dataflow Streaming Engine, Flexible Resource Scheduling.
  • IAM, Quotas, and Permissions
    • In this module, we talk about the different IAM roles, quotas, and permissions required to run Dataflow
  • Security
    • In this module, we will look at how to implement the right security model for your use case on Dataflow.
  • Summary
    • In this course, we started with the refresher of what Apache Beam is, and its relationship with Dataflow.

Taught by

Google Cloud

Reviews

4.9 rating at DataCamp based on 14 ratings

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