AWS Open Source Blog

Category: Expert (400)

Implementing a hub and spoke dashboard for multi-account data science projects

Modern data science environments often involve many independent projects, each spanning multiple accounts. In order to maintain a global overview of the activities within the projects, a mechanism to collect data from the different accounts into a central one is crucial. In this post, we show how to leverage existing services—Amazon DynamoDB, AWS Lambda, Amazon […]

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Setting up Amazon Managed Grafana cross-account data source using customer managed IAM roles

Amazon Managed Grafana is a fully managed and secure data visualization service for open source Grafana that enables customers to instantly query, correlate, and visualize operational metrics, logs, and traces for their applications from multiple data sources. Amazon Managed Grafana integrates with multiple Amazon Web Services (AWS) security services, and supports AWS Single Sign-On (AWS […]

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Enhancing Spinnaker deployment experience of AWS Lambda functions with the Lambda plugin

This post was written by Jason Coffman, Gaurav Dhamija, Vikrant Kahlir, Nima Kaviani, Brandon Leach, Shyam Maniyedath, and Shrirang Moghe. Spinnaker is an open source continuous delivery platform that allows for fast-paced, reliable, and repeatable deployment of software to the cloud. For many AWS customers, including Airbnb, Pinterest, Snap, Autodesk, and Salesforce, Spinnaker is a critical piece of technology that […]

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Deploy AWS CloudFormation stacks with GitHub Actions

At GitHub Universe 2019, we announced that we open sourced four new GitHub Actions for Amazon ECS and ECR. Fast forward to 2020 we are expanding the number of available actions by releasing AWS CloudFormation Action for GitHub Actions. This GitHub Action enables developers and cloud engineers to maintain their infrastructure as code in a […]

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Deploy, track, and roll back RDS database code changes using open source tools Liquibase and Jenkins

Customers across industries and verticals deal with relational database code deployment. In most cases, developers rely on database administrators (DBAs) to perform the database code deployment. This works well when the number of databases and the amount of database code changes are low. As organizations scale, however, they deal with different database engines—including Oracle, SQL […]

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Adopting machine learning in your microservices with DJL (Deep Java Library) and Spring Boot

Many AWS customers—startups and large enterprises—are on a path to adopt machine learning and deep learning in their existing applications. The reasons for machine learning adoption are dictated by the pace of innovation in the industry, with business use cases ranging from customer service (including object detection from images and video streams, sentiment analysis) to […]

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