Course Introduction
1. Course Introduction
Mehran: Hi, and welcome to the third installment of the Dataflow series: Dataflow Operations. My name is Mehran Nazir, and I'm a product manager with Google Cloud Dataflow. You have arrived at the final course of the Dataflow course series, which seeks to provide you all of the skills needed to build your modern data platform on Dataflow. In the Foundations course, we learned about the building blocks of Dataflow, including Shuffle, Streaming Engine, Flexible Resource Scheduling, and Beam portability. We also covered horizontal integrations with Dataflow, including IAM, quotas, and security features. We then move to Developing Pipelines, which explored how you can turn your business logic into a Dataflow Pipeline. We reexamined the building blocks of the Beam SDK, introduced advanced features like state and timers, reviewed best practices, and concluded with a deep dive on SQL, data frames, and notebooks. In the last installment of the Dataflow course series, we will introduce the components the Dataflow operational model. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances. Let's review the outline for the Dataflow Operations course. First, we'll explore Dataflow monitoring, which includes a walkthrough of the various screens of the Dataflow console experience. Next, we'll discuss the logging and Error Reporting integrations, a critical piece of the Dataflow Operations stack. We will review our recommended approach for troubleshooting and debugging Dataflow Pipelines, then explore common causes for Pipeline errors. From there, we will do a thorough examination of performance optimization techniques for Dataflow Pipelines. This module will help you get the most out of your Dataflow jobs. We will discuss testing and continuous integration/continuous deployment, otherwise known as CI/CD, with Dataflow which will help you safely test and roll out changes to your Pipelines. We will move on to reliability with Dataflow Pipelines and discuss methods for building systems that are resilient to corrupted data and data center outages. The final module of this course will cover Flex Templates, a feature that helps data engineering teams standardize and reuse Dataflow Pipeline code. Many operational challenges can be solved with Flex Templates. We will conclude the course with a recap of all the key lessons from the modules. Let's get started.2. Let's practice!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.