Snowflake as a Source Walkthrough

Introduction

This document provides a comprehensive walkthrough in Snowflake as a Source, starting with establishing a new River within Rivery, then proceeding to the selection of extraction modes and the administration of your data storage.
By the end of this guide, you will have a clear understanding of how to effectively harness Rivery's capabilities to extract data from Snowflake and seamlessly merge it into your data ecosystem.

Prerequisite

Please ensure the creation of a correct Connection for Snowflake Source within Rivery.

River Modes

When using Snowflake as a Source, you have the choice to select between two River modes:

• Multi-Tables (Standard Extraction)
• Custom Query.

Multi-Tables (Standard Extraction)

This mode in Rivery combines data from various tables into a single schema before transferring it to the destination. It establishes table relationships to ensure consistent linking and loading. Rivery's Multi-Tables River mode mainly employs SQL queries for transformations, with scheduling or manual triggering options.

To obtain further details about using Multi-Tables (Standard Extraction), kindly refer to our documentation on Databases River Modes.

Custom Query

Rivery's Custom Query River mode empowers users to input data into the platform via personalized SQL queries, offering utmost control over data loading and transformations. Users can specify data and transformations precisely, using SQL, pulling from databases or data warehouses like Snowflake. Data is then scheduled for automatic or on-demand loading into Rivery, ensuring real-time data access.

Please Note:
When opting for the Incremental Extract mode, it's crucial to recognize that you have a choice of only 2 options: Datetime and Running number.

To obtain further details about using Custom Query, kindly refer to our documentation on Databases River Modes.