How to Load Data into Snowflake

Published: May 30, 2024 - 9 min read

Hannah Recker
how to load data into snowflake

Loading data into Snowflake is critical for building a powerful cloud data warehouse. But the process can make or break your Data Cloud’s performance.

In this guide, we’ll share advanced techniques and best practices for optimizing data loading in Snowflake. By the end, you’ll be equipped to load data into Snowflake with confidence and efficiency.

Let’s dive in!

Why Optimized Data Loading Matters

The way you load data into Snowflake can significantly impact query performance, data integrity, and overall cost-effectiveness. When you optimize your data loading processes, you can expect:

  • Faster time-to-insight: With data readily available for analysis, you can uncover actionable insights and make data-driven decisions more quickly. This is especially valuable in fast-paced, competitive business environments where agility is key.
  • Improved data accuracy and consistency: By ensuring data is loaded correctly and completely, you can have greater confidence in the quality and reliability of your analytics. This is critical for building trust in your data and avoiding costly errors due to inconsistent or inaccurate information.
  • Lower data storage and compute costs: By using techniques like file compression, columnar storage, and auto-scaling, you can minimize the storage and compute resources required for data loading. This translates to lower costs and more budget available for other data initiatives.

Loading Data into Snowflake: 4 Methods

Snowflake offers several methods for loading data, each with its own use cases and benefits. The main loading methods are:

  1. Web interface: A user-friendly, point-and-click option for loading small to medium-sized files
  2. SnowSQL: A command-line interface for loading data using SQL commands
  3. Snowpipe: A continuous data ingestion service that automatically loads data from cloud storage
  4. Third-party tools: Partner connectors and ETL solutions that simplify and automate data loading

Let’s take a closer look at each method and when to use them.

Method 1: Loading Data via the Web Interface

The Snowflake web interface is a good choice for loading small to medium-sized files (up to about 50 GB) on an ad hoc basis. It provides a simple, point-and-click experience that doesn’t require any coding.

snowflake web interface

Here’s how to load data via the web interface:

  1. Select the target database, schema, and virtual warehouse for loading the data
  2. Create the destination table using the required SQL commands (e.g., CREATE TABLE)
  3. Navigate to the “Data” tab and select the local file(s) you want to load
  4. Specify the file format, compression type, and other loading options
  5. Preview the data to ensure it looks correct, then click “Load” to start the loading process

Method 2: SnowSQL

SnowSQL is a command-line tool that enables you to load data into Snowflake using SQL commands. It’s a good choice for loading larger files (up to about 1 TB) and for users who are comfortable with SQL.

snowsql command line

To load data with SnowSQL, you’ll typically follow these steps:

  1. Stage the data files in a Snowflake internal or external stage using the PUT command: PUT file://path/to/file.csv @my_stage;
  2. Copy the staged data into the target table using the COPY INTO command:

COPY INTO my_table

  FROM @my_stage/file.csv


  1. Optionally, validate the loaded data by querying the target table and checking row counts, data types, and other attributes.

SnowSQL is best for users who are comfortable with SQL and need more control and flexibility over the data loading process than the web interface provides.

Method 3: Snowpipe

Snowpipe is a serverless data ingestion service that automatically loads data from files as soon as they land in a specified cloud storage location. It’s a good choice for continuous, near-real-time data loading with minimal manual effort.

snowpipe serverless data ingestion

To set up Snowpipe, you’ll need to:

  1. Create an external stage that points to the cloud storage location where data files will be dropped
  2. Define a Snowpipe using the CREATE PIPE command, specifying the target table and external stage
  3. Configure cloud storage event notifications to trigger the Snowpipe when new files arrive

Once configured, Snowpipe will automatically load new data files into the target table as soon as they are detected in the external stage.

This makes it ideal for use cases that require frequent data updates with minimal latency, such as IoT data streaming or clickstream analytics.

Method 4: Your Spreadsheet

Coefficient is a data connector for your spreadsheet. It allows you to pull live data from sources like CRMs, BI solutions and Snowflake, into Excel or Google Sheets in just a few clicks.

With Coefficient, you can load data into Snowflake without leaving your spreadsheet. Here’s how:

Step 1. Install Coefficeint

Before getting started, install Coefficient for your spreadsheet. It only takes a few seconds, and you only have to do it once.

Step 2: Connect to Snowflake

Open the Coefficient Sidebar. Click on the menu icon and select “Connected Sources.”

connect snowflake to spreadsheets to load data

Search for Snowflake and click “Connect”.

connect snowflake to google sheets excel

Enter your account credentials (Snowflake Account Name, Database Name, Username, Password, and Warehouse Name) to complete the connection.

Step 3: Prepare Your Data

Ensure your spreadsheet is organized with a header row that matches the Snowflake table fields and is ready for export.

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Step 4: Start the Export Process

In the Coefficient sidebar, click on “Export to” and select “Snowflake.”

load data into snowflake from google sheets excel with coefficient

Choose the tab and the header row containing the Snowflake field headers. Coefficient automatically selects the sheet you have open, but you can choose a different tab if needed.

choose snowflake header row

Select the table in your database that you want to update.

write data to snowflake

Then select “Insert” from the drop-down.

select to insert data into snowflake

Step 5: Map Fields

Map the sheet columns to Snowflake table fields and click “Save”

confirm snowflake field mappings

Review and confirm your settings. Click

export data to snowflake from spreadsheets

Step 6: Select and Export Rows

Highlight the rows in your sheet that you need to export.

    choose snowflake rows to load

Confirm the rows to update and commit the changes. Note that these actions cannot be undone.

confirm snowflake data load
  • Records that have been updated will have the export status and the date/time stamp of the update in your Sheet.
    successful snowflake data import
  • Data Loading Best Practices

    To ensure your data loading processes are as efficient and reliable as possible, follow these best practices:

    Optimize File Sizes and Formats

    1. Aim for file sizes between 10 MB and 100 MB for optimal loading performance
    2. Avoid loading many small files, which can slow down the loading process; instead, combine small files into larger ones or use compression to reduce file sizes
    3. Use columnar file formats like Parquet or ORC for faster queries and better compression

    Prepare and Clean Data

    1. Remove any unnecessary columns or rows from your source data before loading
    2. Handle missing, null, or malformed values appropriately (e.g., by filtering them out or replacing them with default values)
    3. Ensure data types and formats are consistent and match the target table schema
    4. Apply data compression to reduce file sizes and minimize storage costs

    Maximize Loading Performance

    1. Use appropriately sized warehouses for the data volume and loading requirements; consider using larger warehouses for initial bulk loads and scaling down for incremental loads
    2. Load data during off-peak hours when possible to minimize the impact on other workloads
    3. Take advantage of Snowflake’s auto-scaling and auto-suspend features to automatically adjust warehouse resources based on the loading workload
    4. Monitor data loading jobs and optimize performance as needed by tuning parameters like the number of loading threads or the size of the warehouse

    Validate and Test Loaded Data

    1. Always validate the accuracy and completeness of loaded data by comparing row counts, data types, and values between the source and target
    2. Implement data quality checks to identify and handle issues like missing or inconsistent values
    3. Regularly test the end-to-end data loading process to ensure it remains reliable and performant as data volumes and requirements evolve

    Troubleshooting Common Data Loading Issues

    Despite your best efforts, you may occasionally encounter issues when loading data into Snowflake. Here are some common problems and how to troubleshoot them:

    Data Loading Failures

    If a data loading job fails, the first step is to carefully review the error message returned by Snowflake.

    Common causes of data loading failures include:

    1. File format mismatch: Ensure the file format specified in the COPY INTO command or file format options matches the actual format of the data file(s)
    2. Incorrect file path, name, or permissions: Double-check that the staged file path and name are correct and that the Snowflake user has the necessary permissions to access the files
    3. Data type mismatches: Ensure the data types of the loaded data match the target table schema; if necessary, use the CAST function to convert data types during loading
    4. Insufficient storage space: Ensure there is enough storage space in the target database and schema to accommodate the loaded data

    Slow Data Loading Performance

    If data loading is taking longer than expected or consuming too many resources, consider the following optimizations:

    1. Adjust file sizes and formats: Ensure you’re using optimal file sizes (10-100 MB) and columnar formats like Parquet or ORC for better loading performance
    2. Minimize small files: Combine small files into larger ones or use compression to reduce the number of files being loaded
    3. Tune warehouse size: Ensure the warehouse used for data loading is appropriately sized for the data volume and loading requirements; consider using a larger warehouse for initial bulk loads and scaling down for incremental loads
    4. Schedule loads during off-peak hours: If possible, run data loading jobs during off-peak hours to minimize the impact on other workloads and avoid resource contention
    5. Use auto-scaling and auto-suspend: Take advantage of Snowflake’s auto-scaling and auto-suspend features to automatically adjust warehouse resources based on the loading workload, minimizing costs and maximizing efficiency

    Data Quality Issues

    Even if data loading succeeds, you may encounter data quality issues like missing, inconsistent, or inaccurate values. To identify and resolve these issues:

    1. Validate loaded data: Always validate the accuracy and completeness of loaded data by comparing row counts, data types, and values between the source and target
    2. Implement data quality checks: Use SQL queries or third-party tools to identify and flag data quality issues like null values, duplicates, or out-of-range values
    3. Handle data quality issues: Depending on the nature and severity of the issues, you may need to filter out bad data, replace invalid values with defaults, or refine the data loading process to prevent issues from occurring in the first place
    4. Monitor data quality over time: Regularly monitor the quality of loaded data to identify trends and proactively address issues before they impact downstream analyses or applications

    Load Data into Snowflake in Seconds with Coefficient

    Loading data into Snowflake is a critical first step in unlocking the full value of your data. By following the best practices and techniques outlined in this guide, you’ll be well-equipped to load data efficiently, reliably, and at scale.

    Ready to start loading data into Snowflake? Get started with Coefficient and experience the power of seamless data integration and analysis.

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    Hannah Recker Growth Marketer
    Hannah Recker was a data-driven growth marketer before partying in the data became a thing. In her 12 years experience, she's become fascinated with the way data enablement amongst teams can truly make or break a business. This fascination drove her to taking a deep dive into the data industry over the past 4 years in her work at StreamSets and Coefficient.
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