Eliminate the need to stitch together multiple services and systems by supporting many workloads on unstructured, semi-structured and structured data—all in one platform.
With efficient compression, automatic micro-partitioning and encryption in transit and at rest, Snowflake’s fully managed storage helps you avoid the hassle of securing, backing up and optimizing your data files.
Manage and access files and tables stored in external data lake storage—including open file formats and Apache Iceberg—without having to copy or move data.
Easily integrate third-party data with direct access to live data sets from Snowflake Marketplace, which reduces the costs and burden associated with traditional extract, transform and load (ETL) pipelines and API-based integrations. Or, simply use native connectors to bring data in.
Whether you’re storing data in Snowflake Horizon or your own cloud object storage, you can use Snowflake to understand, govern and monitor your data.
Run pipelines with Snowflake’s elastic multi-cluster compute for reliable performance, cost savings and near-zero maintenance.
Process files or tables in DataFrames simply and securely by using Snowpark to code in Python, Scala or Java—without the need for additional clusters, services or data copies to manage.
Query semi-structured data at the same speed as performing relational queries while preserving the flexibility of schema-on-read.
Snowflake’s near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use. Ongoing performance improvements and native optimizations continue to make costs increasingly efficient.
"By taking advantage of the Snowflake virtual warehouse, we were able to meet our one-to-three-minute SLA for processing pipelines and bring down total runtimes by as much as 75%."
Director, Data Management and Analytics, AMN Healthcare
Estimated annual savings
Pipeline success rate