Managing Snowflake¶

These topics describes the tasks associated with using Snowflake.

• Virtual warehouses — Key concepts and tasks for creating and using virtual warehouses to execute queries and perform DML operations, such as loading and unloading data:

  • Overview of warehouses

  • Multi-cluster warehouses

  • Warehouse considerations

  • Working with warehouses

  • Using the Query Acceleration Service

  • Monitoring warehouse load

• Databases, Tables & Views — Key concepts and tasks related to understanding and working with Snowflake databases and tables:

  • Understanding Snowflake Table Structures

  • Working with Temporary and Transient Tables

  • Introduction to external tables

  • Overview of Views

  • Working with Secure Views

  • Working with Materialized Views

  • Table Design Considerations

  • Cloning considerations

  • Data storage considerations

• Query Data in Snowflake — Key concepts and tasks for executing queries in Snowflake:

  • Working with joins

  • Understanding How Snowflake Can Eliminate Redundant Joins

  • Working with Subqueries

  • Querying Hierarchical Data

  • Working with CTEs (Common Table Expressions)

  • Querying Semi-structured Data

  • Analyzing data with window functions

  • Identifying Sequences of Rows That Match a Pattern

  • Using Sequences

  • Using Persisted Query Results

  • Computing the Number of Distinct Values

  • Estimating Similarity of Two or More Sets

  • Estimating Frequent Values

  • Estimating Percentile Values

  • Querying data using worksheets

  • Canceling Statements

• Date & time data types — Reference information and examples for working with dates, times and timestamps, and time zones in Snowflake:

  • Date and time input and output formats

  • Working with date and time values

• Introduction to Loading Semi-structured Data — Key concepts and tasks for working with JSON and other types of semi-structured data:

  • Introduction to Loading Semi-structured Data

  • Supported formats for semi-structured data

  • Considerations for semi-structured data stored in VARIANT

  • Tutorial: JSON basics for Snowflake

• Introduction to unstructured data — Key concepts and tasks for working with unstructured data:

  • Directory tables

  • REST API for unstructured data support

  • Sharing unstructured data with a secure view

  • Troubleshooting processing of unstructured data

• String & binary data types — Reference information and examples for working with binary data in Snowflake:

  • Binary input and output

  • Using binary data

• Snowflake Time Travel & Fail-safe — Key concepts and tasks for understanding how Snowflake maintains access to deleted and modified data, and also how Snowflake enables data recovery in the event of loss:

  • Understanding & using Time Travel

  • Understanding and viewing Fail-safe

  • Storage costs for Time Travel and Fail-safe

• Introduction to Streams and Tasks — Key concepts and tasks for transforming and optimizing loaded data for analysis:

  • Introduction to Streams

  • Introduction to tasks

  • Introduction to dynamic tables

• Introduction to business continuity & disaster recovery — Key concepts and tasks for replicating and failing over objects across multiple Snowflake accounts, as well as redirecting client connections, for business continuity and disaster recovery:

  • Introduction to replication and failover across multiple accounts

  • Redirecting client connections

• Sample Data Sets — Key concepts and tasks for using the sample data sets provided with Snowflake:

  • Using the Sample Database

  • Sample Data: TPC-H

  • Sample Data: OpenWeatherMap — Deprecated