The Customer
Integra Connect is a leading healthcare technology company specializing in precision medicine and value-based care solutions for oncology and other specialty practices across the United States.
The Project
Cloud Data Warehouse Modernization On Snowflake
Overview
Integra Connect, a leader in precision medicine and specialty care, particularly oncology, aimed to revolutionize its data processing and analytics capabilities. With their existing systems facing scalability and performance limitations, they approached Improving to modernize their cloud data warehouse. The project involved migrating to Snowflake, implementing best practices, and optimizing data processing pipelines to handle larger data volumes efficiently and cost-effectively.
The Business Challenge
Integra Connect was grappling with significant challenges related to their traditional data processing platform. The existing system, based on SQL Server and legacy healthcare platforms, suffered from long processing times, high costs, and limited scalability. They needed to process data from numerous oncology clinics and healthcare partners more efficiently. Additionally, they faced difficulties in scaling their platform within Microsoft Azure, and their vision of real-time data ingestion and analytics was hindered by the current infrastructure.
Our Solution
Improving collaborated closely with Integra Connect to address these challenges through a comprehensive strategy. We started by developing a robust architecture and implementation plan for the new platform, focusing on security, data access, and integration with Snowflake. Our engineering teams worked on migrating and transforming data pipelines, ensuring seamless integration with existing systems. We also re-engineered specific processes that were not suited for a distributed cloud environment, optimizing them to leverage Snowflake's capabilities fully.
Technologies & Methodology Used
Snowflake: Core platform for data warehousing, providing scalability and performance.
DBT: Used for data transformation and data DevOps, including DBT Cloud for runtime monitoring and job management.
Microsoft Azure: Utilized for initial cloud infrastructure and managed instances.
Azure Data Factory: Managed job kickoffs and handovers to DBT Cloud.
Power BI: Integrated for business intelligence and analytics.
SQL Server and SSIS: Legacy platforms used for initial data ingestion and integration.
The Business Benefits
Improved Performance: Reduced data processing times from several days to minutes, significantly enhancing operational efficiency.
Cost Optimization: Transitioned to a consumption-based model, reducing cloud consumption costs and eliminating the need for pre-reserved sizes.
Scalability: Enabled handling of larger data volumes and improved scalability to meet future growth and market expansion goals.
Real-time Data Processing: Implemented real-time or more frequent data ingestion, providing timely analytics and insights.
Enhanced Data Strategy: Allowed for rapid development and deployment of new analytics and features, shortening development cycles.
Innovation Enablement: Laid the foundation for future innovations and market penetration by modernizing the data architecture.
Partnerships
We maintained regular collaboration with Snowflake and DBT, including their sales and product development teams. These partnerships were crucial in ensuring the implementation of best practices and the integration of new functionalities as needed. The joint effort allowed us to leverage the expertise of both Snowflake and DBT, ensuring a smooth transition and optimal performance of the new platform.
Lessons Learned
Migration Challenges: Legacy problems can hinder new platform implementations, requiring careful re-engineering.
Scalability Considerations: New architectures must be designed with scalability in mind to handle future growth.
Cost Management: Transitioning to consumption-based models can significantly reduce costs if managed correctly.
Real-time Processing: Implementing real-time data ingestion can drastically improve analytics capabilities and decision-making.
Collaboration Importance: Regular collaboration with partners like Snowflake and DBT is essential for successful implementation.
Continuous Improvement: Modernizing data architecture is an ongoing journey that enables continuous innovation and improvement.
Why Improving
The Cloud Data Warehouse Modernization on Snowflake project for Integra Connect exemplifies Improving's expertise in data and cloud solutions. By addressing performance, scalability, and cost challenges, we enabled Integra Connect to achieve their ambitious data strategy goals and unlock future innovations. Our unique approach of re-engineering processes and leveraging strong partnerships ensured the project's success, demonstrating our commitment to delivering impactful solutions for our clients.
