## Overview [Snowflake](https://www.snowflake.com/en/) is a cloud-based data platform that provides a fully managed, scalable, and high-performance data warehouse solution. It separates storage, compute, and services, enabling flexible and cost-efficient data processing. <br> ![[Tooling Snowflake.png]] *Snowflake user interface for monitoring query performance* ## Features - **Cloud-Native Architecture**: Runs on AWS, Azure, and Google Cloud without infrastructure management. - **Separation of Compute & Storage**: Scales independently to optimize costs and performance. - **Multi-Cluster Warehouses**: Enables automatic scaling to handle high concurrency. - **Support for Semi-Structured Data**: Handles JSON, Parquet, Avro, and more without transformation. - **Zero-Copy Cloning**: Duplicates data instantly without additional storage costs. - **Time Travel & Fail-Safe**: Restores previous data versions to prevent data loss. - **Secure Data Sharing**: Enables real-time data exchange without data movement. - **Automatic Optimization**: Handles indexing, partitioning, and performance tuning internally. ## Applications - **Enterprise Data Warehousing**: Stores and analyzes massive datasets efficiently. - **Data Lakes**: Integrates with cloud storage for structured and semi-structured data. - **Real-Time Analytics**: Processes streaming data with Snowpipe for fast insights. - **Business Intelligence**: Connects with visualization tools like Tableau, Looker, and Power BI. - **Machine Learning & AI**: Integrates with tools like DataRobot, H2O.ai, and Python libraries. - **Data Sharing & Collaboration**: Enables secure and instant data sharing across organizations. ## Best Practices - **Warehouse Sizing & Scaling**: Choose the right warehouse size and enable auto-suspend to optimize costs. - **Query Optimization**: Use clustering keys and result caching for improved performance. - **Data Governance**: Implement role-based access control (RBAC) and masking policies. - **Efficient Data Loading**: Use bulk loading with COPY INTO for batch processing. - **Stream & Batch Processing**: Leverage Snowpipe for continuous data ingestion. - **Monitoring & Auditing**: Utilize ACCOUNT_USAGE views for query performance and cost tracking. ## Pricing ​Snowflake's pricing is consumption-based, focusing on three main components: 1. **Compute Costs**: - **Virtual Warehouses**: These are clusters of compute resources for data processing tasks. Warehouses range from X-Small (1 credit/hour) to 6X-Large (512 credits/hour), with per-second billing and a 60-second minimum. 2. **Storage Costs**: - **Data Storage**: Billed at a flat rate per terabyte (TB) per month, with rates varying by cloud provider and region. For example, on-demand storage in the AWS US East (Northern Virginia) region is priced at $40 per TB per month. 3. **Data Transfer Costs**: - **Data Egress**: Transferring data out of Snowflake may incur charges, especially when moving data across different regions or cloud providers. Transfers within the same cloud provider and region are typically free, but cross-region or cross-cloud transfers are subject to fees. ​ Snowflake offers multiple editions with distinct features and pricing:​ - **Standard Edition**: Provides core data warehousing capabilities.​ - **Enterprise Edition**: Includes all Standard features plus advanced security and data governance.​ - **Business Critical Edition**: Offers enhanced security features suitable for highly regulated industries.​ - **Virtual Private Snowflake (VPS)**: Designed for organizations requiring dedicated infrastructure for maximum security.​ The cost per credit varies based on the chosen edition, cloud provider, and region. For instance, in typical US AWS regions:​ - **Standard Edition**: $2.00 per credit​ - **Enterprise Edition**: $3.00 per credit​ - **Business Critical Edition**: $4.00 per credit​ - **VPS Edition**: $6.00 per credit ## Usage We currently have one client, [[Freeday]], running on Snowflake. One big advantage of the Snowflake setup vs. the usual BigQuery is the single-tenant server capacity, which always guarantees capacity, this creates higher uptime numbers. In this setup, we extract all data using [[Tooling/Extraction/Airbyte]] and connect it to the semantic models in [[Cube]], which routes the metrics into [[Preset]] for the Internal Analytics, [[Google Sheets]] for its company reporting and into its embedded analytics.