Client Overview

The client is a major software provider for home and community-based healthcare providers and Medicaid-managed care payers. They aimed to modernize its data warehouse to achieve near real-time data availability for reporting and analytics, improved ability to react quickly to source schema changes, and enhanced scalability through a cloud-based architecture. The objective was to utilize Amazon Redshift as the data warehouse due to existing investments in Amazon Web Services (AWS) infrastructure.

Recommendation and Implementation

Due to the high stakes of the solution selection decision, a thorough proof-of-concept was recommended, assembling a team of subject matter experts in data architecture, data engineering, and DevOps. After reviewing the current data model, an architecture review was conducted, identifying and costing potential tool options.

The engineers set up a development environment and ran an eight-week proof-of-concept that included:

  • Testing both historic and continuous replication data loads to the new warehouse.
  • Creating infrastructure and automation scripts.
  • Building sample dashboards.

The exercise demonstrated that the client’s requirements could be best met by a combination of AWS tools – RedShift and Data Migration Services – coupled with Talend, an Extract, Transform, Load (ETL) tool selected and licensed on behalf of the client after testing several market-leading ETL tools.

Production Environment Setup

With the client’s approval to move forward, the following actions were taken:

  • Set up the production environment.
  • Managed the conversion of historic data loads.
  • Established monitoring, alert, and reconciliation mechanisms.
  • Created both automated scripts and procedural manuals for steady-state management.

After the new warehouse was live, support continued, running both the old and new data pipelines in parallel for a few weeks to ensure performance met expectations. This phase also included:

  • Building out more robust dashboards for monitoring.
  • Training stakeholders on dashboard usage before disconnecting the old data warehouse and pipeline.

Outcome

Anoteros’ approach to the complex data warehouse migration gave the client confidence in vendor and tool selection decisions, ensuring that it could meet their needs. Ultimately, the goals were met with the implementation of the new data solution, including near-time replication from the source systems to the data warehouse.

Throughout the project, expertise in cloud-based data warehousing complemented the in-house data team’s understanding of the existing data model, enabling them to independently operate and maintain the new environment at project completion.

Contact Information

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