AWS Database Blog
Category: Intermediate (200)
Encrypt Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL database with minimal downtime
Recently one of our customers, asked us to help them encrypt their unencrypted Amazon Relational Database Service (Amazon RDS) for PostgreSQL. In this post, we show a solution to create an encrypted database from their existing unencrypted database and cut over with the least disruption to applications. This solution uses database Snapshot and PostgreSQL logical […]
Optimize the cost of your Amazon ElastiCache for Redis workloads
Customers often use a caching service like Amazon ElastiCache to boost application performance and scale. In this post, we go over 5 different recommendations to optimize the cost of your Amazon ElastiCache for Redis clusters. Amazon ElastiCache for Redis, a fully managed caching service, is a cost-effective solution that provides ultra-fast response for modern applications. […]
Rate limited bulk operations in DynamoDB Shell
DynamoDB Shell is (ddbsh) an open-source command line interface for Amazon DynamoDB. For a simple introduction, refer to Query data with DynamoDB Shell – a command line interface for Amazon DynamoDB. The ddbsh README.md file has detailed command and usage examples. One of the objectives of ddbsh is to provide a simple and intuitive environment […]
Amazon RDS: Snapshot, restore, and recovery demystified
Amazon Relational Database Service (Amazon RDS) is a managed relational database service offering. The managed service automation of Amazon Web Services (AWS) takes care of installation, storage provisioning, storage management, OS and database patching, and snapshot and restore of database instances. Offloading the undifferentiated heavy lifting of database infrastructure management to AWS helps you focus […]
Improve application performance on Amazon RDS for MySQL and MariaDB instances and MySQL Multi-AZ DB clusters with Optimized Writes
Amazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale deployments of MySQL and MariaDB in the cloud. Amazon RDS customers run different types of workloads on Amazon RDS for MySQL and Amazon RDS for MariaDB. You can use read replicas to scale read options of their workloads, but scaling […]
Working with date and timestamp data types in Amazon DynamoDB
July 2024: This post was reviewed and updated for accuracy. Amazon DynamoDB customers often need to work with dates and times in DynamoDB tables. Querying tables on the basis of year, month, day, or even hours and minutes for real-time workloads has become a necessity for organizations. Therefore, it’s important to be able to query […]
Generate suggestions for leisure activities in real time with Amazon Neptune
DoGet App is a mobile application that connects friends for sharing in-person moments together. Suggestions for activities to engage in with friends are presented to users in card deck format: swiping up indicates no interest in an activity, and swiping down indicates interest and prompts a follow-up on when a user is available (such as […]
Best practices for migrating SQL Server MERGE statements to Babelfish for Aurora PostgreSQL
To migrate a SQL Server database to Babelfish for Aurora PostgreSQL, you usually need to perform both automated and manual tasks. The automated tasks involve automatic code conversion using the Babelfish Compass tool with the -rewrite flag and data migration using AWS Database Migration Service (AWS DMS). The manual tasks involve database compatibility check using […]
Model molecular SMILES data with Amazon Neptune and RDKit
Modeling chemical structures can be a complex and tedious process, even with the help of modern programs and technology. The ability to explore chemical structures at the most fundamental level of atoms and the bonds that connect them is an essential process in drug discovery, pharmaceutical research, and chemical engineering. By infusing chemical research with […]
Build hypothetical indexes in Amazon RDS for PostgreSQL with HypoPG
Indexes in PostgreSQL are essential for improving the performance of database queries. They serve as data structures that organize and optimize the retrieval of information from database tables. By creating indexes on specific columns, PostgreSQL can locate and access relevant data more efficiently. Indexes work by creating a separate data structure that contains a sorted […]









