Build and share Streamlit data apps that run inside Snowflake.
Snowflake Profiled
A fully managed cloud data platform with separate storage and compute, known for its SQL warehouse, live data sharing and marketplace.
At a glance
Snowflake is a fully managed SaaS data platform. It is best known for bringing cloud data warehousing into the mainstream with separate storage and compute. It has since added Python and ML (Snowpark, Snowflake ML), open formats (Apache Iceberg tables), ingestion (Snowpipe, Openflow) and GenAI (Cortex AI). Snowflake runs on AWS, Azure and Google Cloud.
Where it fits
- Strongest when: SQL-centric analytics teams want near-zero administration, easy scaling for concurrency, and live data sharing with partners.
- Consider carefully when: workloads centre on heavy Spark engineering, or data must stay entirely in open formats managed by other engines. Iceberg tables narrow this gap.
Key concepts
- Organisation → Account → Database → Schema → Object
- Virtual warehouses: compute sized T-shirt style (XS … 6XL), billed in credits per second
- Editions: Standard, Enterprise, Business Critical and VPS; features such as masking policies depend on edition
- Secure Data Sharing: consumers query the provider's data with no copy
In the tool landscape (3)
- Snowflake — Fully managed cloud data platform with separate storage and compute, known for SQL warehousing and data sharing.
- Snowflake Horizon Catalog — Snowflake's built-in governance for compliance, security, discovery and lineage.
- Streamlit — Open-source Python framework for building data apps, also available inside Snowflake.
Products (13)
Data Apps & Development
Data Engineering (Spark & Notebooks)
Notebooks in Snowsight for Python, SQL and Markdown, running on warehouses or container runtimes.
DataFrame APIs and runtimes for Python, Java and Scala that run inside Snowflake's compute.
Data Integration (ETL/ELT)
Preview
A managed data integration service, based on Apache NiFi, with connectors for structured and unstructured sources.
Continuous, serverless file ingestion (Snowpipe) and low-latency row-level ingestion (Snowpipe Streaming).
Data Science & ML
An end-to-end ML toolkit including Feature Store, Model Registry and model serving.
Data Sharing & Marketplace
Privacy-preserving, multi-party data collaboration on Snowflake.
A marketplace for third-party data, data services and Snowflake Native Apps.
Data Warehouse
Independently sized and scaled compute clusters that run SQL and Snowpark workloads against shared storage.
Generative AI & Agents
LLM functions, search and natural-language analytics running inside Snowflake's security boundary.
Governance & Catalog
Built-in governance for compliance, security, privacy, discovery and lineage across Snowflake objects.
Lakehouse
Tables in Apache Iceberg format stored in customer-owned cloud storage, managed by Snowflake or by an external catalog.
Transformation & Modelling
Declarative tables defined by a query and a target freshness (lag); Snowflake handles incremental refresh.
Upcoming events
Summit · In person
Snowflake Summit 2027
Mon, 7 Jun 2027 – Thu, 10 Jun 2027
San Francisco, United States
- Price not stated
From the blog
Data sovereignty in cloud data platforms: what to check before you choose
5 August 2025
Residency is not sovereignty. A practical guide to the legal, operational and architectural questions to ask of Microsoft Fabric, Databricks, Snowflake, and any other cloud data platform.
Capability snapshot
25 native · 5 partial
| Capability | Support | Notes |
|---|---|---|
| Platform & Deployment | ||
| Cloud availability | Native | AWS, Azure and Google Cloud, in many regions; cross-cloud replication supported. |
| Deployment model | Native | Fully managed SaaS. |
| Pricing model | Native | Credit-based compute, plus storage. |
| Storage & Formats | ||
| Open table formats | Native | Native Snowflake table format plus Apache Iceberg tables (Snowflake-managed or externally managed catalog). |
| Storage/compute separation | Native | A founding architectural principle (multi-cluster shared data). |
| Query external data in place | Native | External tables, Iceberg tables with external catalogs, external stages. |
| Compute & Workloads | ||
| SQL warehousing | Native | |
| Serverless compute | Native | No clusters to manage; warehouses auto-suspend and auto-resume. Many serverless features exist (tasks, Snowpipe). |
| Spark & notebooks | Partial | Snowflake Notebooks and Snowpark (DataFrame API) rather than managed Apache Spark; check the status of Spark-compatibility options. |
| Languages | Native | SQL, Python, Java, Scala (via Snowpark), JavaScript (UDFs/procedures). |
| Integration & Pipelines | ||
| Managed ingestion connectors | Native | Snowpipe / Snowpipe Streaming, Openflow connectors. Verify Openflow status. |
| Declarative transformation pipelines | Native | Dynamic Tables define transformations declaratively with target lag. |
| Orchestration | Partial | Tasks and task graphs; often paired with external orchestrators. |
| Streaming / real-time | Native | Snowpipe Streaming for low-latency ingestion. |
| Governance & Security | ||
| Unified catalog | Native | |
| Lineage | Native | Lineage in Snowsight (Horizon). Verify edition requirements. |
| Row/column-level security | Native | Row access policies and dynamic data masking (Enterprise edition or higher). |
| Sharing & Collaboration | ||
| Cross-organisation data sharing | Native | Secure Data Sharing, with no data copied inside the same region; cross-region/cloud through replication. |
| Data marketplace | Native | |
| Data clean rooms | Native | |
| Analytics, ML & AI | ||
| Native BI / dashboards | Partial | Snowsight dashboards and Streamlit in Snowflake; not a full BI suite, and usually paired with Tableau, Power BI, etc. |
| ML lifecycle | Native | Snowflake ML (Feature Store, Model Registry, model serving on Snowpark Container Services). |
| LLM functions in SQL | Native | Cortex AI SQL functions. |
| Natural-language querying / data agents | Native | Cortex Analyst and Snowflake Intelligence. Verify GA status. |
| Operational (OLTP) database | Partial | Hybrid Tables (Unistore); Snowflake Postgres announced. Verify status. |
| Unification | ||
| Lifecycle coverage | Partial | Strong on data, sharing, governance and AI, and adequate on integration; visualisation is limited to Snowsight rather than a full BI tool, and orchestration is thin. |
| Shared storage | Native | A single managed storage layer serves every workload, with Iceberg tables extending the same engine to data held outside Snowflake. |
| Shared governance | Native | One policy model across every workload, enforced by the engine itself at query time through masking and row access policies rather than documented and hoped for. Worth knowing when budgeting rather than when comparing architecture - masking, row access policies, classification, quality monitoring and lineage need Enterprise edition or higher. |
| Shared metadata and identity | Native | One account-wide catalogue across workloads, reaching beyond Snowflake only through Snowflake-mediated paths such as Iceberg tables, the Iceberg REST catalog API and catalog-linked databases. |
| Sold as one platform | Native | One platform billed in credits, though which features are available at all depends on the edition purchased. |