Snowflake New Web Interface — Snowsight

Snowflake New Web Interface — Snowsight

Snowsight is open for all accounts running on Amazon Web Services and Microsoft Azure, while Google Cloud Platform is planned to be supported.

Introduction

Snowflake has recently introduced a new, desktop-quality web user interface for ad-hoc data analysis and verification that supports fast querying, SQL auto-completion, automatic stats, and various charts for data visualization. The application is still in preview mode, meaning the account admins need to click on the Preview App menu and enable Snowsight. As of writing the article, Snowsight is open for all accounts running on Amazon Web Services and Microsoft Azure, while Google Cloud Platform is planned to be supported.

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Using Snowsight

According to Snowflake, the main use cases for Snowsight are data validation, explorative SQL queries, and "quick charts" to share with team members, while the classic BI tools such as Tableau, Qlik, or Power BI are more geared towards corporate reporting needs, business users and complex dashboards

_Figure 1: When to use Snowsight (Source: Snowsight Academy) _

SQL Auto-Completion

One of the most attractive features of Snowsight compared to the classic Worksheet is that it supports context-sensitive auto-completion.

For instance, if you are typing a SELECT statement and start entering curr, Snowsight will recommend matching SQL functions that can be used in that place. 

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Figure 2: Auto-completion with SQL functions

Or, in the FROM section of the SQL statement, you can choose the names of the available tables offered by auto-completion:

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Figure 3: Auto-completion with table names

Automatic Statistics

When the SQL queries are executed, the Results section contains the data retrieved by the query and in addition to that, Snowsight will also provide statistics such as how many rows have been retuned, how long running the query took, how well the columns in the table are populated, etc. This can help system admins or data analysts to quickly identify potential data quality issues.

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