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A top 10 independent broker-dealer began encountering issues with performance and reliability within AccountView, the application that allows investors to access accounts. CapTech collaborated with the company to implement an improved version of the application with a cloud-hosted server that could scale appropriately to meet its needs.


The independent broker-dealer was using an outdated version of AccountView, which was hosted via an SQL Server backend with a heavy MuleSoft API implementation. The legacy implementation relied on proprietary technologies and on-premises infrastructure that made scalability and maintenance difficult. Seeing an opportunity to improve operational footing and address current challenges, the CapTech team developed a plan to migrate AccountView to the cloud and streamline the application to mitigate future architectural issues and solve scalability constraints.


After evaluating several options, CapTech selected AWS Glue as the best method for Extract, Load, Transform (ELT) and capturing changes as they occur. CapTech successfully migrated dozens of functions that were previously encapsulated within the SQL Server/Mulesoft environment into the AWS Cloud implementation. Additionally, CapTech utilized canary testing to release a few code changes to a small number of end users at a time to test for accuracy. This approach reduces customer impact and risk ahead of a full-scale deployment into production.

The selected DevOps approach utilized TeamCity to generate NuGet packages, which would then be deployed to various AWS environments via Octopus worker agents. Within the D&A project space, there were two primary vectors for deployments:

  • Scripts/infrastructure for AWS Glue jobs and workflow orchestration
  • Data Definition Language (DDL) and Data Manipulation Language (DML) for updates to the various RDS Postgres databases

The AWS Glue deployments utilized Terraform for infrastructure deployments, as well as S3 agents to update Python script files. The DDL/DML deployments utilized dbUp, which provides version management and other safety features which prevent unexpected changes to the underlying data structures.

Performance and reliability of these new processes have been within expected parameters, with additional scalability now possible via the decoupled cloud services-based approach. As an example, the team saw success in scaling up Glue jobs when worker memory constraints were encountered. This approach allows the independent broker-dealer to seamlessly sunset the SQL server and transition customers to the new system without any awareness that a change has taken place.


The cloud-hosted architecture enabled cost-effective automated scaling and the migration of 100+ APIs to AWS.


  • Decomposed monolithic data structure into domain-driven model
  • Provided target architecture for moving to the cloud
  • Migrated APIs from MuleSoft to .NET, hosted in Kubernetes
  • Created pipelines to migrate on-prem data to AWS database