Category: Learning Levels

In this post, we provide a comprehensive guide for addressing performance considerations when migrating Oracle databases from Exadata to Amazon RDS for Oracle. We explore methods to analyze Exadata workload characteristics, including determining Smart IO usage, examining database-level I/O patterns, and identifying SQLs that utilize Exadata-specific features. We also discuss various alternatives available on RDS for Oracle to mitigate potential performance impacts.

Amazon DynamoDB Accelerator (DAX) is a fully managed, in-memory cache for DynamoDB. By using DAX with DynamoDB, you can improve the latency for read requests in your application. In this post, we discuss how to improve latency and reduce cost when using DynamoDB for your read-heavy applications.

In this post we demonstrate, how you can extend your existing AD infrastructure and Kerberos authentication to Amazon RDS for Db2.

FundApps, founded in 2010, is one of the pioneers in the Regulatory Technology (RegTech) space, which includes compliance monitoring and reporting. FundApps decided to rearchitect their environment and transform it to a cloud-based architecture on AWS to better support the growth of their business. For more information, see Faster, cheaper, greener: Pick three — FundApps modernization journey. In this post, we focus on the persistence layer of the FundApps regulatory data service. You learn how FundApps improved the service scalability, reduced cost, and streamlined operations by migrating from SQL Server database to a cloud-centered solution combining Amazon Aurora Serverless v2 with Babelfish for Aurora PostgreSQL and Amazon Simple Storage Service (Amazon S3).

Recently, Amazon RDS launched the ability to shrink storage volumes using Amazon RDS Blue/Green Deployments – a nice addition to the list of new use cases that Blue/Green Deployments now supports. In this post, we cover how to use the new storage volume shrink feature in Amazon RDS Blue/Green Deployments to minimize the downtime required to perform the storage size reduction operation. We also review various mechanisms to monitor the progress of storage shrink and best practices on how to arrive at the optimal storage size for your shrink storage task.

You can create an Amazon RDS for Db2 instance by using the AWS Management Console, AWS Command Line Interface (AWS CLI), AWS CloudFormation, Terraform by Hashicorp, AWS Lambda functions, or other methods. One of the prerequisites for creating an RDS for Db2 instance is to configure the virtual private cloud (VPC) appropriately. This post shows how to create a VPC with best practices for any Amazon RDS database in general and Amazon RDS for Db2 in particular through a one-click automated deployment.

With AWS DMS, you can use data validation to make sure your data was migrated accurately from the source to the target. If you enable validation for a task, AWS DMS begins comparing the source and target data immediately after a full load is performed for a table. In this post, we describe the custom framework we built on top of AWS DMS validation tasks to maintain data integrity as part of the ongoing replication between source and target databases.

In How the Amazon TimeHub team designed resiliency and high availability for their data replication framework: Part 2, we covered different scenarios handling replication failures at the source database (Oracle), AWS DMS, and target database (Amazon Aurora PostgreSQL-Compatible Edition). As part of our resilience scenario testing, when there was a failover between the Oracle primary database instance and primary standby instances, and the database opened up with RESETLOGS, AWS DMS couldn’t automatically read the new set of logs in case of a new incarnation. In this post, we dive deep into the solution the Amazon TimeHub team used for detecting such a scenario and recovering from it. We then describe the post-recovery steps to validate and correct data discrepancies caused due to the failover scenario.

In How the Amazon Timehub team built a data replication framework using AWS DMS: Part 1, we covered how we built a low-latency replication solution to replicate data from an Oracle database using AWS DMS to Amazon Aurora PostgreSQL-Compatible Edition. In this post, we elaborate on our approach to address resilience of the ongoing replication between source and target databases.

With the recent addition of physical replication as an option for RDS Blue/Green Deployments, you can overcome most of the limitations of logical replication. This makes physical replication particularly well-suited for use cases like minor version upgrades, schema changes (DDL operations) in the blue environment, and storage adjustments. In this post, we delve into the advantages of using physical replication in RDS for PostgreSQL blue/green deployments to simplify database operations and scale with application demands. We explore the key benefits of physical replication and provide a step-by-step guide to help you get started with this new capability.