AWS DevOps & Developer Productivity Blog
Category: Analytics
Exploring Telemetry Events in Amazon Q Developer
As organizations increasingly adopt Amazon Q Developer, understanding how developers use it is essential. Diving into specific telemetry events and user-level data clarifies how users interact with Amazon Q Developer, offering insights into feature usage and developer behaviors. This granular view, accessible through logs, is vital for identifying trends, optimizing performance, and enhancing the overall […]
How A/B Testing and Multi-Model Hosting Accelerate Generative AI Feature Development in Amazon Q
Introduction In the rapidly evolving landscape of Generative AI, the ability to deploy and iterate on features quickly and reliably is paramount. We, the Amazon Q Developer service team, relied on several offline and online testing methods, such as evaluating models on datasets, to gauge improvements. Once positive results are observed, features were rolled out […]
A new Spark plugin for CPU and memory profiling
Introduction Have you ever wondered if there are low-hanging optimization opportunities to improve the performance of a Spark app? Profiling can help you gain visibility regarding the runtime characteristics of the Spark app to identify its bottlenecks and inefficiencies. We’re excited to announce the release of a new Spark plugin that enables profiling for JVM […]
How to unit test and deploy AWS Glue jobs using AWS CodePipeline
This post is intended to assist users in understanding and replicating a method to unit test Python-based ETL Glue Jobs, using the PyTest Framework in AWS CodePipeline. In the current practice, several options exist for unit testing Python scripts for Glue jobs in a local environment. Although a local development environment may be set up […]
Using AWS DevOps Tools to model and provision AWS Glue workflows
This post provides a step-by-step guide on how to model and provision AWS Glue workflows utilizing a DevOps principle known as infrastructure as code (IaC) that emphasizes the use of templates, source control, and automation. The cloud resources in this solution are defined within AWS CloudFormation templates and provisioned with automation features provided by AWS […]
Monitoring and management with Amazon QuickSight and Athena in your CI/CD pipeline
One of the many ways to monitor and manage required CI/CD metrics is to use Amazon QuickSight to build customized visualizations. Additionally, by applying Lean management to software delivery processes, organizations can improve delivery of features faster, pivot when needed, respond to compliance and security changes, and take advantage of instant feedback to improve the […]