AWS Database Blog

Federated query support for Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL

PostgreSQL is one of the most widely used database engines and is supported by a very large and active community. It’s a viable open-source option to use compared to many commercial databases, which require heavy license costs. Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL are AWS managed offerings that take away the heavy lifting […]

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Database reset in Neptune is a two-step process.

Resetting your graph data in Amazon Neptune in seconds

As an enterprise application developer building graph applications with Amazon Neptune, you may want to delete and reload your graph data on a regular basis to make sure you’re working with the latest changes in your data, such as new relationships between nodes, or to replace test data with production data. In the past, you […]

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Amazon DocumentDB (with MongoDB compatibility) read autoscaling

Amazon Document DB (with MongoDB compatibility) is a fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads. Its architecture supports up to 15 read replicas, so applications that connect as a replica set can use driver read preference settings to direct reads to replicas for horizontal read scaling. Moreover, as […]

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Migrating SQL Server to Amazon RDS using native backup and restore

Running Microsoft SQL Server on Amazon Relational Database Service (Amazon RDS) automates time-consuming administration tasks such as installation, disk provisioning and management, patching, minor and major version upgrades, and backup and recovery of your SQL Server databases. Amazon RDS also offers high availability using a Multi-AZ solution, replicating data synchronously across different Availability Zones. Currently, […]

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Building a knowledge graph with topic networks in Amazon Neptune

This is a guest blog post by By Edward Brown, Head of AI Projects, Eduardo Piairo, Architect, Marcia Oliveira, Lead Data Scientist, and Jack Hampson, CEO at Deeper Insights. We originally developed our Amazon Neptune-based knowledge graph to extract knowledge from a large textual dataset using high-level semantic queries. This resource would serve as the […]

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Automating Hyperledger Fabric chaincode deployment on Amazon Managed Blockchain using AWS CodePipeline

Amazon Managed Blockchain is a fully managed service that makes it easy to create and manage blockchain networks using the open-source blockchain framework Hyperledger Fabric. In this post, we explore how to use AWS CodePipeline and related services to automate the deployment of chaincode. Chaincode is a program that typically handles business logic agreed to […]

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How to get started with Neptune ML

Amazon Neptune ML is an easy, fast, and accurate approach for predictions on graphs. In this post, we show you how you can easily set up Neptune ML and infer properties of vertices within a graph. For our use case, we have a movie streaming application and we want to infer the the top genres […]

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Announcing Amazon Neptune ML: Easy, fast, and accurate predictions on graphs

We’re thrilled to announce the availability of Amazon Neptune ML, an easy, fast, and accurate approach for predictions on graphs. Neptune ML is a new capability that uses graph neural networks (GNNs), a machine learning (ML) technique purpose-built for graphs. With GNNs, you can improve the accuracy of most predictions for graphs by over 50% […]

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Creating an Amazon CloudWatch dashboard to monitor Amazon RDS and Amazon Aurora MySQL

Amazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale a relational database in the cloud. A highly performant database is key to delivering latency SLAs, so monitoring is critical. Amazon CloudWatch is a monitoring and observability service built for DevOps engineers, developers, site reliability engineers (SREs), and IT managers. […]

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Optimizing costs in Amazon RDS

One of the key benefits  of AWS is the flexibility to provision the number of resources an application needs and scale up or down as requirements change. This requires monitoring your current resource utilization and having a policy to take actions when required. Without these proactive measures in place, you can end up under-provisioning or […]

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