AWS Database Blog

Category: Database

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.

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.