AWS Step Functions large-scale parallel data processing | Serverless Office Hours скачать в хорошем качестве

AWS Step Functions large-scale parallel data processing | Serverless Office Hours

Find out all about the new AWS Step Functions Distributed Map for large-scale parallel data processing. Product Manager Denise Young and Solutions Architect Brian Zambrano join James Beswick to show off the functionality. See how you can build Step Functions to coordinate large-scale parallel workloads within your serverless applications. You Learn how to iterate over millions of objects such as logs, images, or .csv files stored in S3 with up to 10,000 parallel workflows to process data! We are also available to answer any of your serverless questions. Guests: Denise Young   / deniseyoung   Brian Zambrano   / brianzambrano   Host: James Beswick   / jbesw   🆕📢 What’s new in AWS Serverless? https://s12d.com/whats-new ✍️ Serverless AWS Compute blog posts: https://s12d.com/serverless-blogs Stream links: Demo on GitHub: https://github.com/aws-samples/aws-st... Documentation: https://docs.aws.amazon.com/step-func... Launch Blog – https://aws.amazon.com/blogs/aws/step... Workflow pattern on Serverlessland - https://serverlessland.com/workflows/... #serverless #aws ✅ SUBSCRIBE TO THIS CHANNEL: https://s12d.com/youtube 🌥️🌥️ SERVERLESS OFFICE HOURS 🌥️🌥️ 📃 Playlist: https://s12d.com/soh-youtube A weekly live office hours to learn about building serverless applications. We cover a separate topic each week including what's new with serverless, deep dives into feature launches and best practices, and explore real world use-cases. Join us live to ask us anything you want about serverless technologies and applications. 🌟🌟https://ServerlessLand.com 🌟🌟 Your resource for learning about serverless technology. Learn to use and build apps that scale automatically on low-cost, fully-managed serverless architecture. Chapters: 00:00 Last Week in AWS Serverless 02:22 Introducing Step Functions Distributed Map 11: 25 Demo calculating highest weather data 38:30 Q&A