This page gives you a quick, practical overview of Snowflake—a cloud data platform for storage, processing, and analytics.
You’ll learn when to use it, how the architecture works, and which features matter in real projects.
In short, the service separates compute from storage, which means you can scale each independently.
Moreover, multi-cluster warehouses keep performance predictable during spikes.
Because of that design, teams run BI, ELT, and data-science workloads without constant tuning.
The platform separates storage, compute, and services for elastic scale and governance.
If you plan to launch analytics quickly, start small: load data to a stage, transform with SQL, and size a warehouse.
For an end-to-end example, see our guide on building a warehouse pipeline.
In addition, the official docs cover security, sharing, and cross-cloud operations.
Governance spans roles, policies, and historical recovery for safer collaboration.
Scroll to the flashcards for a rapid refresher. Then, follow the links at the end to practice with real code and queries.
❄️ Snowflake Flashcards
❄️ What is it?
A cloud-native platform for storage, processing, and analytics with elastic scale.
🧊 How is the architecture designed?
Multi-cluster, shared data design that separates storage, compute, and services.
📦 What are Warehouses?
Virtual compute engines that run queries and scale independently of storage.
🗃️ What is a Database?
A container for schemas, tables, views, and other objects inside the platform.
💾 What are Stages?
Temporary or permanent locations to load/unload data from S3, GCS, or Azure Storage.
📊 What is Time Travel?
Query, restore, or clone historical data within a retention window.
🧠 What is Zero-Copy Cloning?
Create instant copies of objects without duplicating storage.
📈 How does scaling work?
Resize a warehouse or add clusters automatically to meet demand.
🔒 What security features exist?
End-to-end encryption, RBAC, SSO, masking policies, and compliance controls.
🔗 Semi-structured data?
Yes—use the VARIANT type for JSON, AVRO, XML and query with SQL.
🔄 What is Data Sharing?
Share live data across accounts securely without copying.
📡 Which clouds are supported?
AWS, Microsoft Azure, and Google Cloud with cross-cloud capabilities.
To begin, size a small warehouse, load a sample dataset, and validate performance.
Next, enable masking policies and role-based access before sharing.
Finally, set up data sharing or replication if you operate across regions or clouds.
