When data is spread across S3, Azure Blob, and Google Cloud Storage, analytics teams face several issues. These span inconsistent formats, duplicated ETLExtract, transform, load (ETL) is a fundamental data integration process where data is collected from various sources, converted into a unified and clean format, and then stored in a central destination like a data warehouse for analysis and decision-making., and brittle governance.
Snowflakehttps://www.snowflake.com/en/ provides a unified data cloud experience across AWS, Azure, and Google Cloud. While each deployment operates independently within its cloud region, Snowgrid connects them to enable seamless governance, replication, and data sharing. It separates key concerns across three layers. Durable object storage in each cloud holds the data. Elastic compute clusters run queries where needed. A cloud services layer manages metadata, security, and query coordination. Together, these layers create a uniform developer and analyst experience without requiring the wholesale movement of data.
The illustration below visualizes this shift as data scattered across silos moves through ingestion, processing, and analysis. It is ultimately centralized into a unified data platform with Snowflake.
