Optmizing Query Performance with Clustering Keys in Snowflake
This blog explores how Clustering Keys Data Pruning, and the Search Optimization Service (SOS) enhance query efficiency in Snowflake. It explains how clustering keys physically organize data into micro-partitions enabling faster queries by reducing unnecessary scans. Data pruning leverages metadata to skip irrelevant partitions further improving performance.
dbt and Snowflake Integration
This article explores the integration of dbt with Snowflake, a comprehensive guide to using dbt with Snowflake. It covers the main concepts of dbt, including models, materializations, sources, tests, documentation, macros, and packages. It also discusses Snowflake-specific features like zero-copy cloning, time travel, and tasks. Additionally, it covers advanced concepts like incremental models, snapshots, seeds, and custom materializations. Finally, it provides best practices for using dbt with Snowflake.
Snowflake Fundamentals
An overview of Snowflake, a cloud-based data warehouse platform. This article explains Snowflake's architecture, key features, and how it differs from traditional data warehouses. Learn about its separation of storage and compute, support for structured and semi-structured data, and built-in security features.
What is Snowflake?
An overview of Snowflake, a cloud-based data warehouse platform. This article explains Snowflake's architecture, key features, and how it differs from traditional data warehouses. Learn about its separation of storage and compute, support for structured and semi-structured data, and built-in security features.
