How to Import CSV into Redshift: 5 Practical Methods

Redshift is optimized for columnar analytics. To keep loads fast and clean, you need the right ingest path. Here are five practical ways to import CSVs into Redshift, from simple COPY to managed pipelines.

1) COPY from S3 (Recommended)

Stage CSV in S3, then run COPY—fastest and most reliable approach.

COPY public.users
FROM 's3://my-bucket/import/users.csv'
CREDENTIALS 'aws_access_key_id=...;aws_secret_access_key=...'
CSV IGNOREHEADER 1
DELIMITER ','
QUOTE '"';

Best when: standard approach; use IAM roles instead of keys in production.

2) Redshift Query Editor v2

Web UI to run COPY commands; helpful for quick, manual loads when you already staged in S3.

Best when: manual/one-off loads with UI comfort; small/medium files.

3) DBeaver / SQL Workbench-J

Use your DB tool to run COPY (from S3) or INSERTs for small data. Good for familiar workflows.

Best when: developer tooling preference, moderate control, not massive files via INSERT.

4) AWS Glue / Managed Pipelines

Managed ETL to move CSV from S3 into Redshift with jobs, scheduling, and monitoring.

Best when: recurring loads, need scheduling/monitoring, low-ops.

5) Python + redshift-connector

Full control for validation, transforms, retries, and observability.

pip install redshift-connector

import redshift_connector

conn = redshift_connector.connect(
    host="redshift-cluster.xxxx.aws-region.redshift.amazonaws.com",
    database="dev",
    user="app",
    password="secret",
    port=5439,
)

with conn.cursor() as cur:
    cur.execute(
        """
        COPY public.users
        FROM 's3://my-bucket/import/users.csv'
        IAM_ROLE 'arn:aws:iam::123456789012:role/RedshiftCopyRole'
        CSV IGNOREHEADER 1
        DELIMITER ','
        QUOTE '"'
        """
    )
conn.commit()

Best when: recurring loads, custom validation/transform, need retries/logging.

Choosing the Right Approach

Standard/fast: COPY from S3.
Manual UI: Query Editor v2.
Developer tools: DBeaver/SQL Workbench-J.
Managed/recurring: AWS Glue.
Custom: Python + redshift-connector.

Where FileFeed Fits

When CSV imports become part of onboarding or product flows, schemas drift, validation rules grow, retries and audit logs matter, and engineers become the bottleneck. FileFeed lets you define validation, mapping, and transformations once, reuse across customers/environments, and deliver clean Redshift data without bespoke glue code.

Final Thoughts

Redshift works best with staged CSVs via COPY. Use UI for one-offs, pipelines for recurring. FileFeed keeps ingestion predictable so you do not rewrite the same import logic repeatedly.