Komodo Health Achieves 15% in Cost Savings with Snowflake

Komodo Health is a technology platform company creating the new standard for real-world data and analytics by pairing the industry’s most complete view of patient encounters with enterprise software and machine learning that connects the dots between individual patient journeys and large-scale health outcomes. Across Life Sciences, payers, providers, and developers, Komodo helps its customers unearth patient-centric insights at scale — marrying clinical data with advanced algorithms and AI-powered software solutions to inform decision-making, close gaps in care, address disease burden, and help the enterprise create a more cost-effective, value-driven healthcare system.

Komodo utilizes the Snowflake data platform to store, process, and enrich data from multiple sources. They apply their business logic to unlock deeper insights and generate data products such as Sentinel and Prism.

With guidance from the Snowflake sales engineering team, Komodo implemented best practices that have resulted in improved product performance. The implementation of new features has enabled us to simplify the ETL process, while the adoption of the following Snowflake best practices has also led to a 15% reduction in overall spending. These changes have also been vital in ensuring that Komodo remains a competitive player in the market while continuing to provide high-quality products to our customers.

How Snowflake’s best practices decreased consumption costs for Komodo Health 

By adjusting its architecture to eliminate redundant business processes, replatform applications, and automate and manage application resources, Komodo Health on Snowflake drives efficiency, innovation, and cost-effectiveness. 

Business process elimination

Komodo Health’s data platform uses Dynamic Tables, which automate incremental data refresh with low latency to simplify data engineering workloads, along with Snowflake’s architecture to eliminate inefficient and inflexible processes. This reduced the query run time by 350 hours, or just under 15 days per month.

Replatforming the application to use the right resources

Snowflake warehouses can be resized at any time—even while running—to accommodate the need for more or less compute resources based on the type of operations being performed. By automating the provisioning of Snowflake compute instances, Komodo was able to consolidate 200 4XL pre-existing instances, redistribute workloads, and develop an automated process to archive unused tables. This resulted in faster transaction processing and a 15% cost savings.

Automation and management of the applications resources

By building a centralized dashboard to monitor long-running queries, Komodo enabled alerts to notify users about anomalies. It automated query tagging, enabling data users to track sensitive data for compliance, resource usage, and more, and developed an observability platform for monitoring performance, identifying issues, and managing costs.

Driving innovation using Snowflake features

Komodo optimized its operations in Snowflake by implementing several features, such as Dynamic Tables, Snowpark, Snowpark-optimized warehouses, and SQL forecasting models. Komodo also used multi-cluster compute, resulting in a 70% performance improvement in elapsed query time, with the aforementioned 15% cost reduction. By switching to a SQL-based ML time series model, Komodo improved accuracy and data management, ultimately helping to better forecast cost management in cloud environments.

Hear more about Komodo Health’s optimization journey on Snowflake

Join us at this year’s Snowflake Summit to learn more about how Komodo Health is revolutionizing healthcare data analytics and saving on costs with the Data Cloud. 

The post Komodo Health Achieves 15% in Cost Savings with Snowflake appeared first on Snowflake.


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