How Landauer Achieved Millisecond Latency with AWS OpenSearch Migration

 LANDAUER is the leading global provider of technical and analytical services to determine occupational and environmental radiation exposure and the leading domestic provider of outsourced medical physics services.


The company provides dosimetry services to about 1.8 million individuals globally and is used by 78% of United States hospitals. The company offers tools and support for organizations with potential exposure to ionizing radiation, helping them achieve their radiation safety goals.


LANDAUER’s innovations in radiation safety continue to shape the industry in dosimetry and medical physics.


The Challenge

Landauer faced several challenges that needed to be addressed during the process of migrating their data from a proprietary RDBMS solution to AWS OpenSearch.


The biggest challenge was migrating a huge number of records, in just a couple of days, they were able to migrate approximately 32 million records with minimal downtime.


Additionally, the previous system involved AWS EC2 instances running proprietary RDBMS solutions. The setup incurred a recurring proprietary licensing fee, resulting in a considerable expense.


Moreover, the system required the maintenance of several data indexes to enable filtering and search functionalities, leading to a high latency on their website. The fact that AWS EC2 is not a managed service also meant that the system required manual upkeep from time to time, posing another challenge for Landauer.


That’s when AntStack came in to ensure that the migration was seamless, with no downtime and negligible disruptions to Landauer’s operations.


Our Goals

Landauer sought to migrate their existing database to a low-latency solution while exploring the possibilities of an open-source solution.


The project aimed to accomplish the following goals:


Migrating a huge number of records - close to 32 million records from a proprietary RDBMS to AWS OpenSearch.

Keeping the production system live during the migration with no to negligible downtime.

Implementing a new system that can handle high volumes of data, enable filtering and search capabilities, and reduce latency.

Saving costs and improving scalability by adopting a serverless architecture. Adding additional features as and when the requirements arise.



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