Comparing Starburst with Snowflake: Choosing the Right Data Strategy

Two major players in the data space — Starburst and Snowflake — offer distinct, equally valuable approaches to data management. While both solutions provide powerful analytics capabilities, the way they handle data access, scalability and cost can make a huge difference for an organization’s overall data strategy.

In this rendition of our C-Suite Series, we’ll break down the key differences between Starburst’s in-place querying model and Snowflake’s cloud-based data warehouse approach, helping you determine the best fit for your infrastructure and long-term data needs.

At the core of their differences, Starburst and Snowflake take fundamentally different approaches to data access:

• Starburst: Uses a distributed query engine that allows businesses to access and analyze data where it resides, eliminating the need for costly and time-consuming data migrations. This approach minimizes data duplication, reducing storage costs and technical debt.
• Snowflake: This is a cloud-based data warehouse that businesses use to store and analyze their data. To work with it, companies need to move their data into Snowflake’s system first. It runs on Amazon Web Services and Microsoft Azure, offering flexible storage and computing power. However, it requires ongoing data migration to keep everything up to date.

For companies with vast, multi-source data environments spanning on-prem, cloud, and hybrid infrastructures, Starburst’s ability to query in place can reduce complexity while speeding up the valuable time it takes to uncover insight. Meanwhile, Snowflake provides a streamlined solution for those seeking an all-in-one cloud-native platform with structured data storage and built-in management.

If you’re evaluating which solution aligns best with an organization’s needs, several factors should be considered:

Cost Structure

• Snowflake’s consumption-based pricing means organizations pay for the storage and compute resources they use. While this provides flexibility, it can lead to unpredictable costs as workloads grow.
• Starburst operates on a capacity-based pricing model, allowing for upfront budgeting and greater control over long-term expenditures. However, businesses must carefully assess whether they are over-provisioning resources.

Flexibility and Control

• Starburst gives organizations greater control over where and how data is accessed, making it an ideal fit for businesses that need to maintain data sovereignty or operate within compliance constraints.
• Snowflake simplifies data management and governance through a single cloud environment. This offers ease of use and built-in security features like role-based access and encryption.

Scalability and Performance

• Snowflake’s architecture separates storage, compute and cloud services, allowing independent scaling of resources for high availability and elastic performance.
• Starburst’s distributed query engine is designed to handle complex, multi-source data analysis at scale, making it a well-suited choice for companies managing diverse data sets across multiple environments.

The right data strategy goes beyond software. It needs to align with your business goals, fit your infrastructure and support long-term growth. This requires a collaborative approach, working closely with technology partners and providers to assess the right fit.

At Level Solutions Group, we take an all-hands-on-deck approach to supporting clients through this process. We work directly with businesses and technology providers like Starburst to ensure that the capabilities they need match the solutions they implement. By evaluating the full data ecosystem, we help companies make informed decisions that optimize performance, cost efficiency and future scalability.

The right data strategy is one that evolves with your business. While Snowflake provides a structured, cloud-based data warehouse with built-in ease of use, Starburst offers a flexible, high-performance solution for organizations that need real-time querying across distributed sources.

By understanding the trade-offs between these two platforms and aligning them with business priorities, organizations can build a future-proofed data strategy — one that delivers insights efficiently, controls costs and adapts to the data landscape that we all know is constantly changing.