Get oriented with Snowflake's core architecture — how its three-layer design separates compute, storage, and services to enable massive scalability. You'll explore the Snowsight interface and learn how every object in Snowflake fits into a structured hierarchy.

Snowflake Architecture and Editions

Understanding the Architectural Layers

Choosing the right edition

Ways of Working with Snowflake

Running a Query in a Snowflake Notebook

Investigating a Failed Query with Query History

Snowflake Object Hierarchy

Object Hierarchy and Access Control

Navigating an Existing Database Structure

Parameter Hierarchy

Filtering with session variables

How Snowflake Works

Learn how to create and configure the objects that power your Snowflake environment. You'll size and tune virtual warehouses for different workloads, then get hands-on with loading both structured and semi-structured data into tables.

Creating and Managing Database Objects

Setting up Snowy Peak's database structure

Granting the right privileges

Virtual Warehouses: Types, Sizing, and Scaling

Identifying a misconfigured warehouse

Matching workloads to warehouse configurations

Solving a concurrency problem

Loading Structured and Semi-Structured Data

Loading Snowy Peak's user data

Querying Snowy Peak's avalanche event data

Virtual Warehouses and Loading Data

Go under the hood to see how Snowflake physically organizes data using micro-partitions and clustering keys. You'll also compare table types and views to understand when to use each, and explore how Snowflake handles unstructured files alongside traditional data.

Micro-Partitions and Data Clustering

Partition Pruning

Options

Good

Poor

Good vs Poor Clustering Key Candidates

Impact of reclustering

Table Types and Views

Permanent

Transient

Temporary

Table types and data protection

Select the correct view type

Understanding Materialized Views

Working with Unstructured Data

Generating a pre-signed URL

Pre-signed URL use case

Round out your Snowflake foundation by exploring its growing AI/ML ecosystem — from Snowpark and Cortex to Streamlit and Notebooks. You'll also cover essential data protection techniques like Time Travel and cloning, and see how the Snowflake Marketplace enables easy data sharing.

Tools for Every Team

Match Snowflake tools to their use cases

Notebook Properties

Snowflake Cortex and ML Features

Pick the right Cortex function for PDF extraction

Snowflake Cortex vs Snowflake ML

Data Protection and Sharing

Querying historical data with Time Travel

Creating a zero-copy clone

Create and Test a Zero-Copy Clone

Recover a dropped table

Congratulations!

AI/ML, Data Protection, and Sharing

Get a complete picture of how Snowflake is built and why it works the way it does. Starting from the three-layer architecture that separates compute, storage, and cloud services, you'll work your way through virtual warehouses, storage internals, and Snowflake's AI/ML capabilities. By the end, you'll have the foundational mental model needed to work effectively in Snowflake.

Master Snowflake's three-layer architecture and build the mental model you need to work effectively in Snowflake.

Snowflake Architecture

subscriptions_main - business-critical billing data; the team needs up to 30 days of recovery

etl_staging - holds raw data between nightly ETL steps; not needed after the job completes

session_transform - an intermediate result set built inside a stored procedure; not needed outside that call

Table types and data protection

Snowflake Architecture

Hands-on interactive exercise