Stages

Introduction

Stages are a way to load data into Snowflake. You can use stages to load data from files in a variety of formats, including CSV, JSON, and Parquet. You can also use stages to load data from external cloud storage services, such as Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage.

The Snowflake emulator supports stages, allowing you to load data into Snowflake using the same commands and syntax as the Snowflake service. The following operations are supported:

• CREATE STAGE
• DESCRIBE STAGE
• DROP STAGE
• SHOW STAGES

Getting started

This guide is designed for users new to Stages and assumes basic knowledge of SQL and Snowflake. Start your Snowflake emulator and connect to it using an SQL client in order to execute the queries further below.

In this guide, you will create a database and a table for storing data. You will then create a stage to load data into the table.

Download the sample data

You can download the sample data by right-clicking on this link and downloading this in your machine. Unzip the file and save the contents to a directory on your local machine.

Create a database & table

You can create a database using the CREATE DATABASE command. In this example, you can create a database called snowflake_tutorials:

CREATE OR REPLACE DATABASE snowflake_tutorials;

Similarly, you can create a table using the CREATE TABLE command. In this example, you can create a table called employees in snowflake_tutorials.public:

CREATE OR REPLACE TABLE employees (
   first_name STRING ,
   last_name STRING ,
   email STRING ,
   streetaddress STRING ,
   city STRING ,
   start_date DATE
   );

Create a stage

You can now create a stage using the CREATE STAGE command. In this example, you can create a stage called employees_stage:

CREATE OR REPLACE STAGE employees_stage
FILE_FORMAT = csv;

Upload data to the stage

In this example, you can upload the CSV files to the table stage provided for employees table.

• Linux/macOS
• Windows

PUT file://./employees0*.csv @@employees_stage AUTO_COMPRESS=TRUE;

PUT file://C:\temp\employees0*.csv @@employees_stage AUTO_COMPRESS=TRUE;

The expected output is:

+-----------------+--------------------+-------------+-------------+--------------------+--------------------+----------+---------+
| source          | target             | source_size | target_size | source_compression | target_compression | status   | message |
|-----------------+--------------------+-------------+-------------+--------------------+--------------------+----------+---------|
| employees01.csv | employees01.csv.gz |         370 |           0 | NONE               | GZIP               | SKIPPED  |         |
| employees02.csv | employees02.csv.gz |         364 |           0 | NONE               | GZIP               | SKIPPED  |         |
| employees03.csv | employees03.csv.gz |         407 |           0 | NONE               | GZIP               | SKIPPED  |         |
| employees04.csv | employees04.csv.gz |         375 |           0 | NONE               | GZIP               | SKIPPED  |         |
| employees05.csv | employees05.csv.gz |         404 |           0 | NONE               | GZIP               | SKIPPED  |         |
+-----------------+--------------------+-------------+-------------+--------------------+--------------------+----------+---------+

Loading files from S3

You can also load data from an S3 bucket using the CREATE STAGE command. Create a new S3 bucket named testbucket and upload the employees CSV files to the bucket. You can use LocalStack’s awslocal CLI to create the S3 bucket and upload the files.

awslocal s3 mb s3://testbucket
awslocal s3 cp employees0*.csv s3://testbucket

In this example, you can create a stage called my_s3_stage to load data from an S3 bucket:

CREATE STAGE my_s3_stage
STORAGE_INTEGRATION = s3_int
URL = 's3://testbucket/'
FILE_FORMAT = csv;

You can further copy data from the S3 stage to the table using the COPY INTO command:

COPY INTO mytable
FROM @my_s3_stage
PATTERN='.*employees.*.csv';