Serverless Data Processing with Dataflow

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Serverless Data Processing with Dataflow

Author: Google Cloud

It is becoming harder and harder to maintain a technology stack that can keep up with the growing demands of a data-driven business. Every Big Data practitioner is familiar with

  • Foundations, which explains how Apache Beam and Dataflow work together to meet your data processing needs without the risk of vendor lock-in
  • Develop Pipelines, which covers how you convert our business logic into data processing applications that can run on Dataflow
  • Operations, which reviews the most important lessons for operating a data application on Dataflow, including monitoring, troubleshooting, testing, and reliability
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  • Identify foundational concepts and components of Dataflow DAG, Beam concepts and Templates.
  • Write pipelines and advanced components such as utility functions, schemas, and watermarks.
  • Perform monitoring, troubleshooting, testing and CI/CD on Dataflow pipelines.
  • Deploy Dataflow pipelines with reliability in mind to maximize stability for your data processing platform

Pre-requisites

A basic understanding of Java or Python programming language is required.

Serverless Data Processing with Dataflow

Building Big Data Applications that Scale.

Serverless Data Processing with Dataflow: Foundations

by Google Cloud

Apr 12, 2021 / 46m

46m

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Description

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow.

In this first course, we start with refreshers of:

  1. what Apache Beam is and its relationship with Dataflow
  2. 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
  3. 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.

Table of contents
  1. Introduction
  2. Beam Portability
  3. Separating Compute and Storage with Dataflow
  4. IAM, Quotas, and Permissions
  5. Security
  6. Summary

Serverless Data Processing with Dataflow: Develop Pipelines

by Google Cloud

Apr 27, 2021 / 1h 58m

1h 58m

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Description

In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

Table of contents
  1. Introduction
  2. Beam Concepts Review
  3. Windows, Watermarks Triggers
  4. Sources & Sinks
  5. Schemas
  6. State and Timers
  7. Best Practices
  8. Dataflow SQL & DataFrames
  9. Beam Notebooks
  10. Summary

Serverless Data Processing with Dataflow: Operations

by Google Cloud

Jun 1, 2021 / 1h 55m

1h 55m

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Description

In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

Table of contents
  1. Introduction
  2. Monitoring
  3. Logging and Error Reporting
  4. Troubleshooting and Debug
  5. Performance
  6. Testing and CI/CD
  7. Reliabiity
  8. Flex Templates
  9. Summary