AWS Database Blog

The need to move data from one location to another in an asynchronous manner is a goal for many enterprises. Use cases might include migrating data to a reporting database, moving applications from on premises to the cloud, storing a redundant copy in another data center, configuring active/active databases across geographic locations, and performing heterogeneous […]

Amazon Relational Database Service (Amazon RDS) for SQL Server makes it easy to set up, operate, and scale SQL Server deployments in the cloud. As a fully managed database service, Amazon RDS for SQL Server takes automated backups of your DB instance during the backup window. If required, you can restore your DB instance to […]

An Oracle to Amazon Aurora PostgreSQL-Compatible Edition or Amazon Relational Database Service (Amazon RDS) for PostgreSQL migration into the AWS Cloud can be a multistage process with different technologies and skills involved, starting from the assessment stage to the cutover stage. For more information about the migration process, see Database Migration—What Do You Need to […]

Blue/green deployment techniques provide near zero-downtime release and rollback capabilities. This requires two identical environments that are running different versions of your application. This also extends to the underlying data layer, where you need two identical Amazon Aurora MySQL-Compatible Edition database clusters running in sync with the same dataset for seamless testing. After completing tests […]

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Neptune’s database engine is optimized for storing billions of relationships and querying with millisecond latency. The W3C’s Resource Description Framework (RDF) model and the popular Labeled Property Graph model […]

Amazon Neptune ML provides a simple workflow for training machine learning (ML) models for graph data. With version 1.0.5.0, Neptune ML delivers additional enhancements to all the steps of this workflow to reduce cost, increase speed, and offer a more flexible modeling experience. Starting with data export and data processing, Neptune ML now provides additional […]

As an application developer building graph applications with Amazon Neptune, your graph data may be evolving on a regular basis, with new nodes and or new relationships between nodes being added to the graph to reflect the latest changes in your underlying business data. Amazon Neptune ML now supports incremental model predictions on graph data […]

Amazon Neptune ML is a feature of Amazon Neptune that brings the power of the state-of-the-art graph neural network (GNN) models to all graph developers. You can use Neptune ML for tasks like node classification, node regression, and link prediction. This allows you to train GNN models powered by the Deep Graph Library (DGL) to […]

With the announcement of Amazon CloudWatch Metric Streams, you can now stream near-real-time metrics data to a destination such as Amazon Simple Storage Service (Amazon S3). Metric Streams supports two primary use cases: Third-party providers – You can stream metrics to partners to power dashboards, alarms, and other tools that rely on accurate and timely […]

In this series of posts (Part 1, Part 2, Part 3 and Part 4) , we compare and contrast the disaster recovery (DR) solutions available for SQL Server on Amazon Elastic Compute Cloud (Amazon EC2) and help you understand the nature of these trade-offs, and the cost and complexity of implementing DR for SQL Server […]