Skill UI

A comprehensive guide to advanced development practices on the Snowflake platform. Covers best practices for SQL (MERGE, semi-structured data, colon prefix rule), modern data pipeline construction (Dynamic Tables, Streams/Tasks, Snowpipe), and deep integration with Cortex AI functions and Snowpark Python. Essential knowledge for data engineers building robust, scalable, and intelligent data applications.

Guides creating, altering, and validating Snowflake semantic views via the Snowflake CLI, covering connection setup, star-schema modeling, metadata comments/synonyms, temporary validation steps, and post-validation querying for reliable semantic layer delivery.

This comprehensive guide demonstrates how to establish a robust Continuous Integration/Continuous Deployment (CI/CD) pipeline for Snowflake data warehousing. It integrates SchemaChange for managing version-controlled database migrations, GitHub Actions for automated testing and deployment triggering, and Terraform for provisioning and managing underlying Snowflake infrastructure (databases, warehouses, roles). Use this when setting up reliable, repeatable, and automated deployment workflows.

This skill provides comprehensive SQL queries and procedures to analyze and optimize Snowflake usage costs. It covers budgeting via resource monitors, right-sizing compute warehouses, tuning auto-suspend settings, and minimizing storage costs. Use it when reviewing billing, implementing cost controls, or reducing credit consumption in your Snowflake environment.

This comprehensive guide details how to implement robust data governance and privacy controls within Snowflake. Learn to apply column-level masking policies, row access policies, and object tagging to secure PII data. It provides practical SQL examples for achieving GDPR/CCPA compliance, managing data subject rights (access, erasure, rectification), and setting up automated data retention tasks.

Master the deployment of applications connected to Snowflake across various cloud environments, including serverless functions (AWS Lambda, Cloud Run) and containers. This guide covers best practices for secure connection management, connection pooling, handling secrets (like private keys), and ensuring application reliability in production settings.

This guide provides minimal working examples for connecting to Snowflake and executing core SQL operations. It comprehensively covers connection setup, running simple SELECT queries, creating databases and tables, and performing parameterized inserts using both Python and Node.js. It is ideal for initial environment setup, testing the Snowflake SDK, or learning fundamental data warehouse interactions.

A comprehensive runbook for rapid incident response to Snowflake outages, query failures, and pipeline breaks. It guides users through systematic triage, checking account usage, managing resource monitors, and performing rollbacks using time travel, ensuring minimal data loss and quick recovery after critical data infrastructure incidents.

This guide provides comprehensive steps to install and configure Snowflake SDKs for Node.js and Python. It covers various authentication methods, including password, secure Key Pair authentication (ideal for CI/CD), and enterprise-grade SSO/OAuth integration. Use this when setting up data pipelines or applications that require reliable and secure connectivity to the Snowflake data warehouse.

This guide establishes a fast, reproducible local development loop for Snowflake. It covers setting up isolated dev warehouses, implementing connection wrappers, and structuring unit and integration tests (including mocking) to ensure rapid iteration and reliable development against a simulated or real Snowflake environment.

This comprehensive guide details how to set up robust, isolated, and secure development, staging, and production environments within Snowflake. It utilizes best practices like zero-copy cloning for efficient data replication, implements granular Role-Based Access Control (RBAC), and defines environment-specific compute resources (warehouses) and data masking policies. This ensures data integrity, compliance, and cost optimization across all stages of the data lifecycle.

A comprehensive guide to optimizing query performance in Snowflake. Learn advanced techniques such as implementing clustering keys, utilizing materialized views, leveraging result caching, and analyzing query profiles. This guide also covers best practices for warehouse tuning and optimizing common SQL anti-patterns to ensure efficient data querying and cost management.