SnowSQL is the software CLI tool used to interact with Snowflake. Using SnowSQL, you can control all aspects of your Snowflake Data Cloud, including uploading data, querying data, changing data, and deleting data. This guide will review SnowSQL and use it to create a database, load data, and learn helpful commands to manage your tables and data directly from your CLI.


What You'll Learn

Be sure to check the needed computing requirements before beginning. Also, download the sample files to complete this tutorial and note the folder location for later use.

What You'll Need

What You'll Build

Create a Snowflake Account

Snowflake lets you try out their services for free with a trial account.

Access Snowflake's Web Console

Log in to the web interface on your browser. The URL contains your account name and potentially the region.

Increase Your Account Permission

The Snowflake web interface has a lot to offer, but for now, we'll just switch the account role from the default SYSADMIN to ACCOUNTADMIN. This increase in permissions will allow the user account to create objects.


Download the SnowSQL Installer

SnowSQL can be downloaded and installed on Linux, Windows, or Mac. In this example, we'll download the installer for macOS via the AWS endpoint. If you're using a different OS or prefer other methods, check out all the ways to get SnowSQL here.

curl -O​<bootstrap-version>/darwin_x86_4/snowsql-<snowsql-version>-darwin_x86_64.pkg

Specify and ​ number within the cURL command.

The example below is a cURL command to the AWS endpoint for a macOS to download the bootstrap version 1.2 and SnowSQL version 1.2.9.

curl -O​


Install SnowSQL Locally

After completing these steps, you'll be ready to use SnowSQL to make a database in the next section.

Sign in From the Terminal

snowsql -a <account-name> -u <username>

The -a flag represents the Snowflake account, and the -u represents the username.

Create a Database and Schema

create or replace database sf_tuts;

The command ​create or replace database​ makes a new database and auto-creates the schema ‘public.' It'll also make the new database active for your current session.

To check which database is in use for your current session, execute:

select current_database(),

Generate a Table

create or replace table emp_basic (
  first_name string ,
  last_name string ,
  email string ,
  streetaddress string ,
  city string ,
  start_date date

Running ​create or replace table​ will build a new table based on the parameters specified. This example reflects the same columns in the sample CSV employee data files.


Make a Virtual Warehouse

create or replace warehouse sf_tuts_wh with
  auto_suspend = 180
  auto_resume = true

After creation, this virtual warehouse will be active for your current session and begin running once the computing resources are needed.


With the database objects ready, you'll employ SnowSQL to move the sample data onto the emp_basic table.

If you have not already done so, you can download the sample files here:

Download Sample Data

Stage Files With PUT


put file:///tmp/employees0*.csv @<database-name>.<schema-name>.%<table-name>;


put file://c:\temp\employees0*.csv @sf_tuts.public.%emp_basic;
put file:///tmp/employees0*.csv @sf_tuts.public.%emp_basic;

Here is a PUT call to stage the sample employee CSV files from a macOS file:///tmp/ folder onto the emp_basic table within the sf_tuts database.


LIST Staged Files

list @<database-name>.<schema-name>.%<table-name>;

To check your staged files, run the list command.

list @sf_tuts.public.%emp_basic;

The example command above is to output the staged files for the emp_basic table. Learn more LIST syntax ​here​.


COPY INTO​ Your Table

copy into emp_basic
  from @%emp_basic
  file_format = (type = csv field_optionally_enclosed_by='"')
  pattern = '.*employees0[1-5].csv.gz'
  on_error = 'skip_file';

After getting the files staged, the data is copied into the emp_basic table. This DML command also auto-resumes the virtual warehouse made earlier.


The output indicates if the data was successfully copied and records any errors.

With your data in the cloud, you need to know how to query it. We'll go over a few calls that will put your data on speed-dial.

select * from emp_basic;

Here is an example command to select everything on the emp_basic table.


Sifting through everything on your table may not be the best use of your time. Getting specific results are simple, with a few functions and some query syntax.

select * from emp_basic where first_name = 'Ron';

This query returns a list of employees by the first_name of ‘Ron' from the emp_basic table.


select email from emp_basic where email like '';

The like function checks all emails in the emp_basic table for au and returns a record.


Snowflake supports many ​functions​, ​operators​, and ​commands​. However, if you need more specific tasks performed, consider setting up an ​external function​​.

Often data isn't static. We'll review a few common ways to maintain your cloud database.

If HR updated the CSV file after hiring another employee, downloading, staging, and copying the whole CSV would be tedious. Instead, simply insert the new employee information into the targeted table.

Insert Data

​INSERT​ will update a table with additional values.

insert into emp_basic values
  ('Clementine','Adamou','','10510 Sachs Road','Klenak','2017-9-22') ,
  ('Marlowe','De Anesy','','36768 Northfield Plaza','Fangshan','2017-1-26');

Drop Objects

In the command displayed, insert is used to add two new employees to the emp_basic table.


drop database if exists sf_tuts;

drop warehouse if exists sf_tuts_wh;

After practicing the basics covered in this tutorial, you'll no longer need the sf-tuts database and warehouse. To remove them altogether, use the drop command.

For security reasons, it's best not to leave your terminal connection open unnecessarily. Once you're ready to close your SnowSQL connection, simply enter !exit.

Use SnowSQL for Your Application

You've created a Snowflake account, set up a cloud database with compute resources, and migrated data to the cloud with SnowSQL. Nice work! There are many advantages to using the cloud. Now that you know how easy getting started with Snowflake is, it's time to consider your next steps.

With your firm grasp of loading data with SnowSQL, start using it to run your application. Continue by ​developing an application​ with SnowSQL to learn how to connect your data to a Python application. If you already have application data, consider migrating it to the cloud with the same steps we used to complete the emp_basic table. Snowflake's tools and documentation are extensive and give you the power of cloud computing.

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