Data Cloud Architecture
The Snowflake Data Cloud is built on a cloud-native architecture that is not limited by legacy technology. Snowflake’s architecture seamlessly enables a variety of workloads across public clouds and regions, and it can handle near-unlimited amounts and types of data with low latency. Here’s how the Data Cloud’s architecture helps users easily collaborate.
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Cloud-Built Architecture
The Snowflake Data Cloud is purpose-built for the cloud, bringing diverse users, data, applications and workloads together. Snowflake’s unique architecture offers many advantages.
Optimized Storage
With support for structured, semi-structured and unstructured data, organizations can store all of their data at near-infinite scale.
Industry-leading security and governance capabilities
Snowflake Horizon (features in preview) enables organizations to secure their most sensitive data, apps and more with a built-in, unified set of compliance, security, privacy, interoperability and access capabilities in the Data Cloud.
Seamless operation across regions and clouds
Snowgrid, Snowflake’s cross-cloud technology layer, connects business ecosystems across regions and clouds. Using Snowgrid, geographically distributed teams can collaborate more efficiently, simplify governance to help with regulatory compliance, and easily replicate data, pipelines and accounts for greater business continuity.
A more secure, resilient infrastructure
Snowflake’s cloud architecture separates the compute, storage and service layers to provide superior data protection and exceptional service resilience.
Elastic Multi-Cluster Compute
Near-limitless compute resources are available on demand. The single engine eliminates concurrency issues, automatically scales up and down based on usage, and scales out to support concurrent users, data volumes and workloads. Support is available for Python, SQL, Java and Scala.
Bringing the power of AI to your business data
With Snowflake Cortex (features in preview), users can quickly and securely analyze data and build AI applications. This intelligent, fully managed service supports industry-leading large language models and vector functions (now in private preview).
The Snowflake Data Cloud vs. Traditional Data Architectures
The data architecture behind the Data Cloud works very differently than those supporting traditional data platforms. Here are some advantages the Data Cloud brings.
Strong compatibility with modern apps
Data analytics and data science apps have taken center stage in many organizations, and provisioning resources to support them is a priority. In addition, AI and machine learning use cases have introduced new demands that legacy platforms may be unable to handle effectively. In contrast, the Data Cloud is built from the ground up to meet the resource requirements of even the most intensive data workloads.
Ability to scale on demand
Spikes in demand during periods of peak usage create resource contention issues that shut some users out, snarling productivity and stifling the ability to extract data-driven insights with speed and efficiency. Snowflake’s Data Cloud automatically expands to accommodate surges, providing access to these resources when and where they’re needed. Automatic resource scaling allows a near-infinite number of users to run concurrent workloads without impacting performance.
Optimized for cost and performance
Organizations still operating on a legacy data platform architecture may be forced to provision a fixed amount of resources to accommodate unpredictable fluctuations in usage. Organizations running on the Data Cloud only pay for the compute and storage they actually use and can scale their resources up and down based on workload needs. Snowflake offers per-second billing, with a suite of features for monitoring usage and controlling costs, providing organizations with full control over their resource consumption.
Fully managed service
Traditional data architectures require ongoing maintenance, including software and hardware updates. The Snowflake Data Cloud is a fully managed enterprise data solution with near-zero administration effort.
Standardizing on One Cloud
For organizations with operations that extend across the major public clouds and regions, maintaining a consistent, uniform experience can be a challenge.
The Snowflake Data Cloud extends across the major public clouds — including Amazon Web Services, Microsoft Azure and Google Cloud Platform — across geographic regions, including North and South America, Europe and the Middle East, and Asia Pacific. A shared metadata layer facilitates the seamless transfer of data and workloads among regions and clouds so users have a consistent, unified experience with Data Cloud workloads running consistently across regions and cloud providers.
Bring Your Data and Workloads Together with Snowflake
Empower your organization with a single platform that harmonizes cloud services and data. The Snowflake Data Cloud brings workloads and data together, allowing you to eliminate the silos and multi-cloud complexity that hold back data-driven innovation and growth.