AWS Database Blog

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.

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.

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.

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.

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.

Parallel indexing in Amazon DocumentDB (with MongoDB compatibility) significantly reduces the time to create indexes. In this post, we show you how parallel indexing works, its benefits, and best practices for implementation.

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.

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.

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.

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.