AWS Database Blog

How London Stock Exchange Group optimised blue/green deployments for Amazon Aurora PostgreSQL Global Database

In this post we share how the London Stock Exchange Group (LSEG) Capital Markets Business unit improved their Blue/Green software deployment methodology, by using continuous logical database replication. We show you the process of implementing a Blue/green deployment architecture using Aurora PostgreSQL Global Database. Specifically, we explore best practices and considerations when configuring the architecture. Blue/green deployment serves as a robust and efficient approach to make sure applications stay resilient and synchronized throughout the process.

AWS DMS homogenous migration from PostgreSQL to Amazon Aurora PostgreSQL

With AWS DMS homogenous migration, you can migrate data from your source database to an equivalent engine on AWS using native database tools. In this post, we show you an example of a complete homogeneous migration process and provide troubleshooting steps for migrating from PostgreSQL to Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL.

Create a Knowledge Graph application with metaphactory and Amazon Neptune

In a previous post, we described how to connect Amazon Neptune to metaphactory, securely, and then how to explore and search the Neptune graph data using metaphactory. In this post, we show how you can use metaphactory to build an end user application using its dynamic model driven components, driven by SPARQL queries.

Configure SSL encryption on an SAP ASE source endpoint in AWS DMS

In this post, we walk you through how to configure Secure Sockets Layer (SSL) encryption between the source endpoints in AWS DMS and an on-premises SAP ASE source for secure data transfer. We also show you the steps for enabling SSL on an on-premises SAP ASE database. Configuring SSL encryption on source endpoints enables encrypting data in transit during the database migration process for enhanced security.

Amazon DynamoDB use cases for media and entertainment customers

In this post, we discuss how Amazon DynamoDB helps media and entertainment customers overcome these challenges for streaming and media supply chain workloads. We also share customer examples, such as Disney, Warner Bros. Discovery, ViacomCBS, and other media applications that are built with DynamoDB.

Adding real-time ML predictions for your Amazon Aurora database: Part 2

In this post, we discuss how to implement Aurora ML performance optimizations to perform real-time inference against a SageMaker endpoint at a large scale. More specifically, we simulate an OLTP workload against the database, where multiple clients are making simultaneous calls against the database and are putting the SageMaker endpoint under stress to respond to thousands of requests in a short time window. Moreover, we show how to use SQL triggers to create an automatic orchestration pipeline for your predictive workload without using additional services.

Automate cross-account backup of Amazon RDS for Oracle including database parameter groups, option groups and security groups

In this post, we showcase AWS Backup and CloudFormation support feature of AWS Backup to automate the backup of Amazon RDS for Oracle, including customized database resources such as database parameter group, option group, and security group across AWS accounts.

How PayU uses Amazon Keyspaces (for Apache Cassandra) as a feature store

PayU provides payment gateway solutions to online businesses through its award-winning technology and has empowered over 500 thousand businesses, including the country’s leading enterprises, e-commerce giants, and SMBs, to process millions of transactions daily. In this post, we outline how at PayU, we use Amazon Keyspaces (for Apache Cassandra) as the feature store for real-time, low-latency inference in the payment flow.