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.
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We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Serverless Data Processing with Dataflow 특화 과정 정보
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 the three V’s of Big Data: volume, velocity, and variety. What if there was a scale-proof technology that was designed to meet these demands?
Enter Google Cloud Dataflow. Google Cloud Dataflow simplifies data processing by unifying batch & stream processing and providing a serverless experience that allows users to focus on analytics, not infrastructure. This specialization is intended for customers & partners that are looking to further their understanding of Dataflow to advance their data processing applications. This specialization contains three courses: 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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Last Updated: September 1, 2015
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