Snowflake Data Engineering with DBT - Online Training Visualpath.pptx

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1. Snowflake Architecture Explained: From Storage to Compute

2. 1: Introduction to Snowflake Architecture Snowflake is a cloud-based data warehouse designed for scalability and performance. It separates compute, storage, and services layers for flexibility. Supports multi-cloud deployment across AWS, Azure, and Google Cloud. Provides seamless data sharing and real-time analytics. Enables automatic scaling and workload optimization .

3. 2: Key Features of Snowflake Architecture Separation of compute and storage layers for efficiency. Automatic query optimization and result caching. Support for structured and semi-structured data (JSON, Avro, Parquet). Zero-copy data sharing and cloning capabilities. Secure data encryption at rest and in transit.

4. 3 : Architecture Overview – The Three Layers 1. Database Storage Layer 2. Compute Layer (Virtual Warehouses) 3. Cloud Services Layer Each layer operates independently for scalability. This design allows concurrent workloads with minimal interference.

5. 4 : Database Storage Layer Handles data storage in optimized, compressed formats. Stores data in micro-partitions for faster query performance. Data is encrypted and managed automatically by Snowflake. Supports time travel and fail-safe recovery options. No manual indexing or partitioning needed by users.

6. 5 : Compute Layer – Virtual Warehouses Virtual warehouses execute queries and perform data operations. Each warehouse is an independent compute cluster. Warehouses can be resized or scaled dynamically. Enables workload isolation for multiple teams or processes. Supports auto-suspend and auto-resume for cost savings.

7. 6 : Cloud Services Layer Manages authentication, metadata, and query optimization . Handles user sessions and resource monitoring. Provides transaction management and security control. Coordinates between compute and storage layers. Delivers elasticity and high availability.

8. 7 : Data Sharing and Integration Allows secure and instant data sharing between Snowflake accounts. No need for data movement or duplication. Integrates with BI tools, ETL platforms, and cloud services. Supports APIs and connectors for real-time data pipelines. Promotes collaboration with Snowflake Data Marketplace.

9. 8 : Performance and Optimization Automatic query optimization using metadata and statistics. Result caching speeds up repeated queries. Clustering and pruning improve data access efficiency. Multi-cluster warehouses handle high concurrency. Built-in monitoring for query performance tuning.

10. 9 : Conclusion and Key Takeaways Snowflake’s architecture delivers speed, scalability, and simplicity. Separation of compute, storage, and services ensures flexibility. Ideal for analytics, data sharing, and data engineering workloads. Cloud-native design supports growth and cost optimization. Empowers organizations to unlock the full potential of their data.

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