Snowflake Concepts

Learn how to subtract days from a date in Snowflake using the powerful DATEADD function.

Learn how to connect dbt and Snowflake to streamline your data transformation and build efficient analytics workflows.

All you need to know to concatenate strings in Snowflake

Ensure accurate analysis by learning how to convert different Snowflake timestamp types, including epoch, TIMESTAMP_NTZ, TIMESTAMP_LTZ, and TIMESTAMP_TZ, to UTC with this advanced technical guide.

A Complete Guide to Snowflake Tasks and Their Use Cases

In Snowflake, you allocate “virtual warehouses” (computing clusters) to execute the SQL database commands that you run on the data platform.

With Snowflake, it’s possible to build an enterprise data warehouse (EDW), an operational data store (ODS), or a team-specific data mart.

In Snowflake, you allocate “virtual warehouses” (computing clusters) to execute the SQL database commands that you run on the data platform.

Databases and schemas ("namespaces") are used to organize data in Snowflake storage, which uses a columnar format internally for analytics.

Snowflake uses credits, which are analogous to CPU nodes, in order to pay for the virtual warehouses that power its analytical query engine.

Snowflake data platform is referred to as a data warehouse or data lake because it separates storage (data) from compute (processing power).

Snowflake uses databases for data storage, while a “Snowflake warehouse” is a virtual computing cluster that processes analytical queries.

Snowflake data platform allows many virtual warehouses in one account, but multi-cluster virtual warehouses are an Enterprise-only feature.

Multi-cluster virtual warehouses auto-scale compute resources based on the demands on the data warehouse. Here’s how they work in Snowflake.

Snowflake’s virtual warehouses are computing clusters that process the data analytics commands you run on Snowflake data analytics platform.