Managing Snowflake¶

These topics describe 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 (QAS)
  • 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
  • Share 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:

  • Use the sample database
  • Sample data: TPC-H
  • Sample Data: OpenWeatherMap — Deprecated