How to Import CSV into Snowflake: 5 Practical Methods

Snowflake separates compute from storage, making it easy to scale ingestion. Here are five reliable ways to load CSVs-from quick, manual uploads to fully automated pipelines.

Method #1: SnowSQL CLI

Best for scriptable, repeatable imports or CI tasks. Use PUT to stage the file, then COPY INTO to load it.

1. Install & connect: Configure SnowSQL with your account, role, and warehouse.
2. Stage the file: run PUT to move the CSV into your user stage.
3. Load the table: run COPY INTO from the staged file.

PUT file:///path/to/your_file.csv @~;
COPY INTO your_table
  FROM @~/your_file.csv
  FILE_FORMAT = (TYPE = 'CSV');

Method #2: Snowflake Web Interface

Good for one-off, manual imports without scripts.

1. Open the UI: Log in and navigate to the target database and table.
2. Load Data wizard: Upload your CSV and map columns as prompted.
3. Review: Confirm the file format (CSV) and run the load.

Method #3: Cloud Storage Staging

Ideal for larger files or recurring feeds. Stage in S3/GCS/Azure, then load via COPY INTO.

1. Upload to storage: Place the CSV in your bucket.
2. Create or use a stage: Point Snowflake to the bucket path.
3. Load: use COPY INTO from the stage (examples below).

-- single-line
COPY INTO your_table FROM @your_stage/your_file.csv FILE_FORMAT = (TYPE = 'CSV');

-- multi-line
COPY INTO your_table
  FROM @your_stage/your_file.csv
  FILE_FORMAT = (TYPE = 'CSV');

Method #4: Airbyte

Great for automated pipelines and ongoing syncs with transformations.

1. Deploy Airbyte: Run locally or in the cloud; open the dashboard.
2. Configure source: Add a CSV source (local path or cloud storage) with format settings.
3. Add Snowflake destination: Provide account, warehouse, database, and schema.
4. Create connection: Map schema, set frequency, define transformations.
5. Sync: Start the job; Airbyte extracts, transforms, and loads into Snowflake.

Method #5: Snowflake Python Connector

For Python workflows needing custom logic before or after load.

1. Install: pip install snowflake-connector-python.
2. Connect: Initialize the connector with user, password, account, warehouse, database, and schema.
3. Stage and load: Execute PUT to stage the CSV, then COPY INTO to load.

pip install snowflake-connector-python

import snowflake.connector

conn = snowflake.connector.connect(
  user="your_username",
  password="your_password",
  account="your_account",
  warehouse="your_warehouse",
  database="your_database",
  schema="your_schema",
)

conn.cursor().execute(
  "PUT file:///path/to/your_file.csv @~"
)
conn.cursor().execute(
  "COPY INTO your_table FROM @~/your_file.csv FILE_FORMAT = (TYPE = 'CSV')"
)

Final Thoughts

Each method fits a different need: quick manual loads, scripted CLI runs, cloud-scale staging, low-maintenance pipelines with Airbyte, or Python-first flows. If you want this automated end to end-with capture, validation, transformations, monitoring, and delivery-consider FileFeed’s Automated FileFeeds and embeddable importer to keep CSV ingestion predictable without extra engineering work.