AWS Database Blog

Perform a two-step database migration from an on-premises Oracle database to Amazon RDS for Oracle using RMAN

In this post, we discuss how to perform a homogeneous migration from an on-premises Oracle database to Amazon Relational Database Service (Amazon RDS) for Oracle. For our solution, we use a two-step approach to migrate the source database to Amazon RDS for Oracle. First, we use RMAN to restore the RMAN backup on an EC2 instance, then we use Data Pump to export data to Amazon S3 and restore that in the RDS for Oracle database.

How Scopely scaled “MONOPOLY GO!” for millions of players around the globe with Amazon DynamoDB

In this post, we show you how Amazon DynamoDB enabled Scopely to quickly respond to their rapid growth with consistent game performance and availability. We also describe how Scopely improved the availability and performance of their matchmaking service with DynamoDB after facing challenges at scale with other solutions.

Use Spring Cloud to capture Amazon DynamoDB changes through Amazon Kinesis Data Streams

In this post, we demonstrate how you can use Spring Cloud to interact with Amazon DynamoDB and capture table-level changes using Kinesis Data Streams through familiar Spring constructs. We run you through a basic implementation and configuration that will help you get started.

Application Continuity for Oracle workloads with Amazon RDS Custom for Oracle

In this post, we show you how to implement Application Continuity in an RDS Custom for Oracle environment using a sample application. We also show you how to test the implementation to see that, when an outage occurs at the database tier, the application recovers and resumes without any data loss—automatically and transparently—along with the database failover. Finally, we show you how to verify the results before cleaning up the environment.

Optimize Amazon RDS costs for predictable workloads with automated IOPS and throughput scaling

In this post, we explain how you can use Amazon RDS IOPS and throughput provisioned settings, automate scaling around monthly and seasonal peaks, and decrease settings during slower weeks. By right-sizing IOPS and throughput levels to your workload’s typical cycles, you can reduce Amazon RDS spend while still getting great performance when you need it most.

Privileged Database User Activity Monitoring using Database Activity Streams(DAS) and Amazon OpenSearch Service

In this post, we demonstrate how to create a centralized monitoring solution using Database Activity Streams and Amazon OpenSearch Service to meet audit requirements. The solution enables the security team to gather audit data from several Kinesis data streams, enrich, process, and store it with retention to meet compliance requirements, and produce relevant alarms and dashboards.

Use Amazon DynamoDB incremental exports to drive continuous data retention

Amazon DynamoDB supports incremental exports to Amazon Simple Storage Service (Amazon S3), which enables a variety of use cases for downstream data retention and consumption. In this post, we show you how to maintain a continuously updating export of your table data by doing a bootstrap full export followed by an ongoing series of incremental exports.

Optimize costs with scheduled scaling of Amazon DocumentDB for read workloads

In this post, we show you two ways to schedule the scaling of your Amazon DocumentDB instance-based clusters to address anticipated read traffic patterns. By aligning your Amazon DocumentDB cluster scaling operations with the anticipated read traffic patterns, you can achieve optimal performance during peak loads and save costs by reducing the need to overprovision your cluster.

Introducing the Advanced Python Wrapper Driver for Amazon Aurora

Building upon our work with the Advanced JDBC (Java Database Connectivity) Wrapper Driver, we are continuing to enhance the scalability and resiliency of today’s modern applications that are built with Python. The Advanced Python Wrapper Driver has been released as an open-source project under the Apache 2.0 License. You can find the project on GitHub. In this post, we provide details on how to use some of the features of the Advanced Python Wrapper Driver.