Snowflake Architecture & Key
Features
The Modern Cloud Data Platform
What is Snowflake?
A fully managed cloud data platform
Supports data warehousing, data sharing, analytics, and data lake functions
Runs on AWS, Azure, and Google Cloud
Key Benefit: Separation of storage and compute
Why Choose Snowflake?
No infrastructure to manage
Pay only for what you use
Built for performance, scalability, and simplicity
Ideal for:
BI tools like Power BI, Tableau
Data lakes and data engineering
ML/AI and data science
Snowflake Architecture Overview
Three-Layer Architecture:
Cloud Services Layer – Manages metadata, security, query optimization
1.
Compute Layer – Virtual warehouses (independent compute clusters)
2.
Storage Layer – Centralized, scalable cloud storage
3.
Cloud Services Layer
Authentication & Access Control
Metadata Management
Query Compilation & Optimization
Infrastructure Management
Enables multi-cluster concurrency
]
Compute Layer (Virtual Warehouses)
Compute engines that run queries independently
Scale compute up/down or pause when not needed
Multiple warehouses can access the same data concurrently
Supports concurrent processing with no performance impact
Storage Layer
Stores all structured and semi-structured data (JSON, Avro, Parquet)
Data is automatically compressed, encrypted, and partitioned
Fully managed: no indexes or manual tuning required
Shared across all users and workloads
Key Features of Snowflake
❄️Separation of Compute & Storage
🔄Auto-scaling & Concurrency
🔐Strong Security (end-to-end encryption, RBAC)
🔗Data Sharing & Marketplace
🔁Time Travel & Fail-safe (recover historical data)
📂Support for Semi-structured Data
Use Case Description
Data Warehousing Centralized, scalable analytics engine
Data Lake Store raw data and transform on read
Data Sharing Securely share data across organizations
Machine Learning Feed clean data to ML models
Real-Time Analytics Power dashboards and reports
Snowflake Use Cases
Contact & Online Training
📢We Provide Online Training on Databricks and Big Data Technologies!
✅Hands-on Training with Real-World Use Cases
✅Live Sessions with Industry Experts
✅Certification Guidance
🌐Visit our website: https://www.accentfuture.com/
📩For inquiries, contact us at: contact@accentfuture.com

Learn snowflake | Online snowflake course

  • 1.
    Snowflake Architecture &Key Features The Modern Cloud Data Platform
  • 2.
    What is Snowflake? Afully managed cloud data platform Supports data warehousing, data sharing, analytics, and data lake functions Runs on AWS, Azure, and Google Cloud Key Benefit: Separation of storage and compute
  • 3.
    Why Choose Snowflake? Noinfrastructure to manage Pay only for what you use Built for performance, scalability, and simplicity Ideal for: BI tools like Power BI, Tableau Data lakes and data engineering ML/AI and data science
  • 4.
    Snowflake Architecture Overview Three-LayerArchitecture: Cloud Services Layer – Manages metadata, security, query optimization 1. Compute Layer – Virtual warehouses (independent compute clusters) 2. Storage Layer – Centralized, scalable cloud storage 3.
  • 5.
    Cloud Services Layer Authentication& Access Control Metadata Management Query Compilation & Optimization Infrastructure Management Enables multi-cluster concurrency ]
  • 6.
    Compute Layer (VirtualWarehouses) Compute engines that run queries independently Scale compute up/down or pause when not needed Multiple warehouses can access the same data concurrently Supports concurrent processing with no performance impact
  • 7.
    Storage Layer Stores allstructured and semi-structured data (JSON, Avro, Parquet) Data is automatically compressed, encrypted, and partitioned Fully managed: no indexes or manual tuning required Shared across all users and workloads
  • 8.
    Key Features ofSnowflake ❄️Separation of Compute & Storage 🔄Auto-scaling & Concurrency 🔐Strong Security (end-to-end encryption, RBAC) 🔗Data Sharing & Marketplace 🔁Time Travel & Fail-safe (recover historical data) 📂Support for Semi-structured Data
  • 9.
    Use Case Description DataWarehousing Centralized, scalable analytics engine Data Lake Store raw data and transform on read Data Sharing Securely share data across organizations Machine Learning Feed clean data to ML models Real-Time Analytics Power dashboards and reports Snowflake Use Cases
  • 10.
    Contact & OnlineTraining 📢We Provide Online Training on Databricks and Big Data Technologies! ✅Hands-on Training with Real-World Use Cases ✅Live Sessions with Industry Experts ✅Certification Guidance 🌐Visit our website: https://www.accentfuture.com/ 📩For inquiries, contact us at: contact@accentfuture.com