How to Load Data into Snowflake Using CSV

Introduction: Why Loading Data into Snowflake Matters

One of the most common tasks in Snowflake is loading data from external files, especially CSV files, into tables. Whether you are just starting out or working on a real-time project, knowing how to load data into Snowflake using a CSV file is essential.

In this guide, you’ll learn the step-by-step process of loading data into Snowflake using a CSV file. We’ll also explain different loading methods, tools, and best practices to follow for error-free data upload.

Understanding the Snowflake Data Loading Process

Before diving in, let’s quickly understand the 3-step process Snowflake follows to load any file:

1. Stage the file – Upload your CSV file to a Snowflake-compatible storage location (called a “stage”).

2. Use the COPY command – Move data from the stage into a table.

3. Validate the data – Check for errors and confirm the data is correctly loaded.

Let’s now explore each part of this process.

Step-by-Step Guide: How to Load CSV into Snowflake

Step 1: Prepare Your CSV File

Before loading, ensure your CSV file:

• Has consistent formatting

• Uses commas as delimiters

• Contains column headers (optional but helpful)

• Is encoded in UTF-8 (default)

📝 Tip: Remove any extra spaces, blank lines, or hidden characters before uploading.

Step 2: Create a Table in Snowflake

Create a target table that matches the columns and data types in your CSV file.

sqlCopyEditCREATE OR REPLACE TABLE employee_data (
  id INT,
  name STRING,
  age INT,
  department STRING
);

Step 3: Choose a Stage to Upload the File

Snowflake requires files to be placed in a stage before loading.

There are 3 types:

Option A: Upload using Web UI (Snowsight)

1. Log in to your Snowflake account.

2. Go to the Database > Table.

3. Click on Load Data.

4. Choose your CSV file from your local system.

5. Select file options (delimiter, skip header).

6. Confirm and upload.

Option B: Upload using SnowSQL CLI

Install SnowSQL and run:

bashCopyEditPUT file:///C:/data/employee_data.csv @%employee_data;

This command uploads the file to the table stage linked to employee_data.

Step 4: Use the COPY INTO Command to Load the Data

Now use the COPY INTO command to load the data from the stage into the Snowflake table.

sqlCopyEditCOPY INTO employee_data
FROM @%employee_data/employee_data.csv
FILE_FORMAT = (TYPE = 'CSV' SKIP_HEADER = 1 FIELD_OPTIONALLY_ENCLOSED_BY = '"');

📝 Tip: Always test your COPY command with a small data sample to catch errors early.

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What is a File Format in Snowflake?

A file format defines how Snowflake should read the file. You can create it once and reuse it for multiple loads.

sqlCopyEditCREATE OR REPLACE FILE FORMAT my_csv_format
TYPE = 'CSV'
FIELD_OPTIONALLY_ENCLOSED_BY = '"'
SKIP_HEADER = 1;

Then use:

sqlCopyEditCOPY INTO employee_data
FROM @%employee_data
FILE_FORMAT = my_csv_format;

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Automating CSV Data Loading with Snowpipe

If you want to automatically load CSV files as soon as they are uploaded (especially in real-time or frequent loads), use Snowpipe.

Key Features of Snowpipe:

• Monitors cloud storage (like S3, Azure Blob)

• Loads data immediately when a new file is detected

• Best for real-time streaming and automation

🧠 Snowpipe requires some setup like:

• External stage pointing to cloud storage

• Notification integration (e.g., AWS S3 events)

• A pipe object to define the data load logic

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Loading CSV from Cloud Storage (S3, Azure Blob)

If your file is stored in AWS S3:

sqlCopyEditCREATE OR REPLACE STAGE s3_stage
URL = 's3://mybucket/data/'
CREDENTIALS = (AWS_KEY_ID = 'xxx' AWS_SECRET_KEY = 'yyy');

COPY INTO employee_data
FROM @s3_stage/employee_data.csv
FILE_FORMAT = my_csv_format;

This is ideal for large files and enterprise pipelines.

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Loading Large CSV Files to Snowflake

If you're working with large CSV files (hundreds of MBs or GBs):

✅ Best Practices:

• Split large files into smaller chunks (100MB–250MB each)

• Compress the files using gzip or bzip2

• Use external stages instead of uploading via UI

• Use multi-threaded COPY for faster performance

Example of compressed file:

sqlCopyEditCOPY INTO employee_data
FROM @s3_stage/employee_data.csv.gz
FILE_FORMAT = (TYPE = 'CSV' COMPRESSION = 'GZIP' SKIP_HEADER = 1);

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Useful Tools for Loading CSV into Snowflake

Popular ETL Tools:

• Hevo Data

• Talend

• Matillion

• Apache NiFi

• Informatica

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Common Errors and How to Fix Them

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SEO-Friendly FAQs (Long-Tail Keywords)

Q1. How do I load CSV file into Snowflake using web interface?
Use Snowsight to upload files into the table stage and then use COPY INTO command to load.

Q2. How to load large CSV to Snowflake efficiently?
Split and compress the file, store it in S3, and load using COPY INTO from external stage.

Q3. Can I automate loading CSV files into Snowflake?
Yes, use Snowpipe to automate the process using cloud storage events.

Q4. What are Snowflake stages and why are they needed?
Stages are temporary or external locations where files are placed before loading. They are essential for the COPY command.

Q5. Is SnowSQL necessary for data loading?
No, but it’s useful for automated scripts and advanced CLI-based data management.

Conclusion: Final Thoughts

Loading data into Snowflake using a CSV file is a crucial first step for anyone working with data on the cloud. Whether you're a student, developer, or business analyst, this skill will help you explore data, generate reports, and build pipelines.

Start small—use Snowsight or SnowSQL—and once you're comfortable, scale up using cloud storage and automation tools like Snowpipe.

By mastering this process, you’re taking the first big step toward becoming a data engineer or Snowflake developer.