Snowflake Data Cloud vs.
Spark-Based Lakehouses
Choose between a self-managing, connected platform delivered as a service, and sets of siloed infrastructure you manage in your environment.
Capabilities |
Spark-based Lakehouse |
Snowflake |
| Self-Managing | Spark-based Lakehouses require intensive planning, setup and management. This means users may spend additional time and energy manually configuring data files, query optimizations, separate compute clusters, security and governance. | Snowflake Data Cloud has near-zero administration, no planned downtime for scaling or maintenance, an elastic multi-cluster compute engine, and unified security and governance. |
| Single Engine | Customers must manage their own pools of compute resources and availability. Multiple engines are required for different use cases and languages, and customers are responsible for selecting runtimes, node types, modes, setting up pools and writing init scripts themselves. | Snowflake includes an elastic multi-cluster compute engine that can both scale up and scale out to support many concurrent workloads against the same data. Customers frequently see lower costs and faster time to value. |
| Governance & Security | Spark-based Lakehouses put the onus of security on the customer. Enabling security controls require many manual processes, such as reviewing alerts and constantly validating controls – which often differ between cloud provider, product edition, object type and compute clusters. | Security is built-in and delivered as part of the service. From tri-secret secure encryption, automatic key rotation, end-to-end encryption for data at rest and in transit to dynamic data masking, Snowflake provides a set of advanced security features embedded within the platform. |
| Collaboration | Spark-based Lakehouses have limited features for collaborating with data inside and outside your organization. This is because the data is frequently siloed by workspace and requires additional configuration to unlock it—and even when unlocked by an additional manual process, only particular objects can be shared, like single tables but not views, functions, or applications. | Snowflake enables cross-cloud, cross-region connectivity using Snowgrid, a proprietary cross-cloud technology layer that forms a grid of interconnected regions. Snowflake also supports sharing data, functions, and apps both inside and outside your organization. |
