Seamless Data Evolution: Migrating from RDS to AWS Redshift for Enhanced Performance and Scalability

Migrating from RDS to AWS Redshift

HermesDMS is a Data Management company specializing in Storing, organizing, and indexing scanned documents. They identified a gap in the market where organizations were facing problems in efficiently retrieving vital information from extensive document repositories. While existing methods were often time-consuming and complex causing operational inefficiencies, they introduced HermesDMS which provided a scalable and cost-effective solution for businesses seeking to streamline document searches. 

Problem Statement

HermesDMS faced many problems, mostly because of the type of database that was being used. Queries were slow because of poorly chosen sort keys and too much shuffling of data, making it time-consuming to get results while using a lot of resources. Irregular distribution of data led to additional challenges, creating difficulties in certain sections of the system to effectively manage large datasets, leading to delays in query processing.

What complicated more in the already exhausted system was the Storage issues. There was no use of any compression method and insufficient use of storage space, especially with big sets of data resulted in more cost. The lack of a regular maintenance policy within the organization has led to poor execution and performance issues. Configuring how resources were used within the chosen database made things worse, causing conflicts, slow performance, and wait times. The system couldn’t handle changes in the amount of work it had to do, making it tough to prioritize important tasks and wasting resources. Also, decisions on how the system worked lacked clear guidelines, leading to ongoing problems in figuring out and fixing issues. Not having clear queues for different tasks affected important jobs, and the system wasn’t ready for sudden increases in work, resulting in slowdowns and even crashes during busy times.

Customer Challenges


In our ongoing efforts within the Hermes DMS, we successfully implemented a range of strategic solutions to overcome HermesDMS’s challenges. Initially, we migrated the data from RDS to Redshift. A key focus was on optimizing query performance by aligning sort keys with typical AWS Redshift query patterns. This approach efficiently organizes data on disk, reducing the need for data distribution during queries and resulting in decreased disk I/O and faster query execution times. Continuous assessments and adjustments of sort keys, based on evolving query trends, ensure ongoing performance improvements.

Our thorough maintenance of Redshift tables played a crucial role in enhancing system efficiency. This involved regular space recovery from deleted rows and scheduled VACUUM operations to prevent unnecessary disk space fragmentation. Additionally, precise query planning was achieved by regularly updating table statistics using the ANALYZE operation, providing accurate insights into data distribution. 


We also optimized the WLM concurrency parameters to efficiently handle diverse workloads, distributing resources effectively among multiple user groups and query types. Informed decisions on WLM settings, based on essential performance metrics, contributed to reduced resource congestion and optimal performance for critical operations. The introduction of priority queue features proved instrumental in meeting various application requirements, ensuring higher-priority tasks are executed promptly and without interference from lower-priority tasks. Lastly, our proactive adaptation to changes in demand, considering historical workload patterns and introducing concurrency scaling techniques, enhances system resilience and responsiveness.


  • Faster Searches: The implementation of smarter data organization techniques has significantly accelerated search processes in the HermesDMS, leading to quicker and more responsive results.
  • Optimized Resource Utilization: Meticulous maintenance of database tables has ensured efficient space usage and a well-organized database structure. This optimization has improved overall system efficiency, allowing tasks to be executed more swiftly and utilizing resources effectively.
  • Enhanced Task Handling: The system’s ability to handle diverse tasks has been enhanced through well-optimized resource sharing. Critical operations now proceed without causing delays for other tasks, contributing to a smoother workflow.
  • Priority Task Execution: The introduction of priority queues has streamlined task management, ensuring that urgent and important jobs are prioritized. This has led to more efficient task execution and improved overall task prioritization.
  • Improved Predictive Capabilities: The system’s enhanced predictive capabilities have made it smarter in anticipating changes in workload. This adaptability allows the system to handle busy periods seamlessly without sacrificing performance.
  • User-Friendly Experience: Overall, these implemented solutions have resulted in a more robust, responsive, and user-friendly DMS system, enhancing the experience for users interacting with the platform.