How to Import CSV Files into PostgreSQL: 5 Practical Approaches

PostgreSQL is fast, reliable, and flexible. The hard part is usually the CSV: changing delimiters, headers, encodings, missing values, duplicates, or type mismatches. Here are five practical ways to import CSVs into Postgres, from quick commands to production-ready flows.

1) Using the COPY Command in PostgreSQL

COPY is the fastest bulk loader. The file must be readable by the Postgres server.

• Best when: server-level access, file already on the server, you want maximum performance.

COPY public.users
FROM '/var/lib/postgresql/import/users.csv'
WITH (
  FORMAT csv,
  HEADER true,
  DELIMITER ',',
  QUOTE '"'
);

2) Using \copy from the psql Client

\copy streams from the client machine—great when the CSV is local or in CI and you do not want server-side file access.

• Best when: running imports from a laptop or CI, avoiding server file permissions, manual or occasional imports.

\copy public.users
FROM '/Users/igor/Downloads/users.csv'
WITH (
  FORMAT csv,
  HEADER true,
  DELIMITER ',',
  QUOTE '"'
);

This avoids most permission issues and is ideal for one-offs from your machine.

3) Importing via pgAdmin (UI)

pgAdmin offers a guided UI: pick a table, choose import, set delimiters/encoding, preview, then load.

• Best when: non-technical teammates need a UI, small/medium imports, visual step-by-step flow.
• Convenient for ad hoc work, but slow or unstable on very large/repeated imports.

4) Using DB Tools like DBeaver or DataGrip

Developer-focused tools include CSV import wizards with mapping and type configuration.

• Best when: you want more control than pgAdmin, you already use these tools daily, you want UI + flexibility.
• Great middle ground for ad hoc imports with moderate complexity.

5) Writing a Custom Import Script

For recurring, business-critical, or user-facing imports, custom scripts/pipelines add validation, transformations, logging, and retries.

Option A: Staging Table with COPY

• Load raw CSV into a staging table (all columns as text).
• Validate/transform/cast into the final table with SQL.
• Robust: bad values stay inspectable in staging; one bad row does not block the batch.

Option B: Python Script with psycopg2

pip install psycopg2-binary

import csv
import psycopg2
from psycopg2.extras import execute_values

# Update with your DSN

dsn = "dbname=app user=app password=secret host=localhost port=5432"

with psycopg2.connect(dsn) as conn:
    with conn.cursor() as cur:
        with open("users.csv", newline="", encoding="utf-8") as f:
            reader = csv.DictReader(f)
            rows = []

            for row in reader:
                rows.append((
                    row["email"],
                    row["first_name"],
                    row["last_name"]
                ))

            execute_values(
                cur,
                """
                INSERT INTO users (email, first_name, last_name)
                VALUES %s
                """,
                rows
            )

• Best when: imports are recurring, validation/transform rules are needed, you need logging/retries/error reporting.

Choosing the Right Approach

• One-time on the server: COPY.
• One-time from local: \copy.
• Manual UI: pgAdmin.
• Developer UI: DBeaver or DataGrip.
• Recurring/production: staging tables or custom pipelines.

Where FileFeed Fits

Once CSV imports become part of your product or onboarding, every customer sends a slightly different file, columns drift, validation rules grow, retries and audit logs are needed, and engineers become the bottleneck. FileFeed lets you define validation, mapping, and transformations once, reuse them across customers and environments, and deliver clean, consistent PostgreSQL data without treating every CSV as a bespoke project.

Final Thoughts

PostgreSQL offers great primitives for CSV import. If imports are rare, the built-in tools are enough. If they are recurring, user-driven, or business-critical, a structured pipeline pays off. FileFeed removes the custom glue work so your team can keep imports reliable without rebuilding the same logic over and over.