Category: AWS Database Migration Service

Careem powers rides, deliveries, and payments across the Middle East, North Africa and South Asia. As Careem grew, so did its data infrastructure challenges. Their monolithic 270 TB Amazon RDS for MySQL database consisting of one writer and five read replicas— experienced performance issues due to increased storage utilization, slow queries, high replica lag, and increased Amazon RDS cost. In this post, we provide a step-by-step breakdown of how Careem successfully implemented a phased data purging strategy, improving DB performance while addressing key technical challenges.

In this post, we present proactive measures for optimizing AWS DMS implementations from the initial setup phase. By using strategic planning and architectural foresight, organizations can enhance their replication system’s reliability, improve performance, and avoid common pitfalls.

We are excited to announce the availability of new features in AWS Database Migration Service (AWS DMS) replication engine version 3.5.4. This release includes two major enhancements: data masking for enhanced security and improved data validation performance. In this post, we deep dive into these two features. Refer to the release notes to see a list of all the new features available in this version.

When you decide to upgrade your PostgreSQL database which is configured as source or target for an ongoing AWS DMS task, it’s important to factor this into your upgrade planning. In this post, we discuss the best practices to handle the AWS DMS tasks during PostgreSQL upgrades to minor or major versions.

AWS Database Migration Service is a cloud service designed to simplify the process of migrating and replicating databases, data warehouses and other data stores. It offers a comprehensive solution for both homogeneous and heterogeneous database migrations, facilitating transitions between different database platforms. The migration process typically involves two major phases: Migration of existing data (full […]

Skello is a human resources (HR) software-as-a-service (SaaS) platform that focuses on employee scheduling and workforce management. It caters to various sectors, including hospitality, retail, healthcare, construction, and industry. In this post, we show how Skello uses AWS Database Migration Service (AWS DMS) to synchronize data from an monolithic architecture to microservices and perform data ingestion from the monolithic architecture and microservices to our data lake.

In this post, we discuss configurations in AWS DMS endpoints and AWS DMS tasks to migrate spatial columns from Oracle to Aurora PostgreSQL-Compatible efficiently.

In this post, we provide details on two PostgreSQL plugins available for use by AWS DMS. We compare these plugin options and share test results to help database administrators understand the best practices and benefits of each plugin when working on migrations.

The zero-ETL integrations for Amazon Redshift are designed to automate data movement into Amazon Redshift, eliminating the need for traditional ETL pipelines. With zero-ETL integrations, you can reduce operational overhead, lower costs, and accelerate your data-driven initiatives. This enables organizations to focus more on deriving actionable insights and less on managing the complexities of data integration. In this post, we discuss the best practices for migrating your ETL pipeline from AWS DMS to zero-ETL integrations for Amazon Redshift.

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.