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October 31, 2025

Mastering PostgreSQL CSV Imports: COPY, \copy, and Beyond

17 mins read

Complete guide to PostgreSQL CSV imports with COPY command, performance tuning, and troubleshooting for database administrators and developers Getting a CSV into PostgreSQL sounds routine—until a single encoding mismatch corrupts 100,000 rows, or a permission error wastes an hour of debugging. The difference between a smooth import and a failed pipeline often comes down to choosing the right method. We tested every major PostgreSQL CSV import approach on version 17.6, reproduced the 6 most common errors, and measured real-world performance (730,000 rows/second with server-side COPY) so you can confidently handle files from 1 MB to 10 GB.

Quick Reference:

  • Fastest method: Server-side COPY FROM (see Performance Results for full benchmarks)
  • Most flexible: Client \copy for remote files
  • Easiest: pgAdmin Import Wizard for small files (under 1 GB)
  • Production apps: Node.js, Python, or Go streaming pipelines
  • Common errors: Extra columns, encoding mismatches, missing permissions
  • Tested on: PostgreSQL 17.6, compatible with 12+

Table of Contents

Prerequisites and Test Environment

All samples and screenshots come from this reproducible setup:

  • PostgreSQL 17.6 (Docker image postgres:17) with default configuration
  • Client tooling: psql 16.9, Docker Compose, pgAdmin 4 v8.x (for GUI walkthrough)
  • Host hardware: Apple M4 Pro (14 cores), 48 GB RAM
  • Test datasets, covering clean and malformed CSV profiles (UTF-8, Latin1, Windows-1252, extra columns, bad quotes)

Recreate the datasets using the generator workflow outlined in the Appendix.

Required PostgreSQL Roles

TaskMinimum role/permission
COPY ... FROM '/path'Superuser or role with pg_read_server_files (and file ownership by postgres OS user)
COPY ... TO '/path'Superuser or pg_write_server_files
\copy (client-side)Database login role with INSERT/SELECT privileges on the target table
Staging tablesCREATE/USAGE on schema + INSERT/DELETE privileges

Security first: apply the least-privilege practices summarized in Security and Compliance when granting roles or exposing directories to COPY.

Connection Helper

Create a disposable .env.testing file for local scripts:

PGHOST=127.0.0.1
PGPORT=5432
PGDATABASE=testdb
PGUSER=postgres
PGPASSWORD=testpass

Then source it (source .env.testing) or prefix the vars when running CLI commands.

Choosing the Right Import Path

Selecting an import method is mostly about where the CSV lives, how big it is, and whether the workflow has to be automated. Start with this decision flow:

  1. Is the file accessible from the PostgreSQL server’s filesystem? Use COPY for maximum throughput.
  2. Do end-users need to upload from their laptops? Reach for \copy or an application-level importer.
  3. Is this a one-off for someone who prefers UI? pgAdmin (or DBeaver/DataGrip) is fast enough for small/medium files.
  4. Do you need validation, mappings, or recurring jobs? Build it into your application (Node.js, Python, Go) or embed ImportCSV.

Import Methods at a Glance

MethodBest ForKey AdvantagesWatch-outs
Server COPY FROMLarge, repeatable loads on the serverFastest path in our tests (see Performance Results); transactional; minimal client loadRequires server filesystem access & correct permissions
Client \copyLocal files, CI pipelines, remote connectionsWorks without server file privileges; easy to scriptSlightly slower; relies on client bandwidth
pgAdmin Import WizardAnalysts & point-and-click workflowsDelimiter/encoding controls in UI; visual feedbackUI struggles beyond ~1 GB; avoid for automation
App pipelines (Node/Python/Go)Custom validation, multi-tenant appsIntegrate business logic, stream to staging tablesRequires robust error handling & backpressure management
ImportCSV widgetProduct teams exposing CSV imports to usersDrop-in React component with validation & mappingBest suited for in-app experiences, not DB seeding

PostgreSQL CSV import methods comparison: COPY vs copy architecture diagram showing server-side and client-side data flow with performance benchmarks

Server-Side COPY: Maximum Throughput

The canonical command looks like this:

COPY customers (full_name, email, signup_source, created_at)
FROM '/datasets/large.csv'
WITH (
  FORMAT CSV,
  HEADER,
  DELIMITER ',',
  ENCODING 'UTF8'
);

Practical considerations:

  • Absolute paths: PostgreSQL resolves paths on the server, not your laptop. On managed services, mount the file (e.g., via S3 extension on RDS) or switch to \copy.
  • File ownership: ensure the CSV is readable by the postgres OS user. Use chown postgres:postgres file.csv or place files in /var/lib/postgresql/ (or a mounted volume as in our Docker setup: /datasets).
  • Column lists: always specify column names if the table has defaults, generated columns, or serial IDs.
  • Transactions: wrap loads in a transaction + staging table (see Advanced Patterns) to validate before committing to production tables.

Performance Tuning Checklist

TuneCommand/SettingWhen
Temporarily relax durabilitySET synchronous_commit = OFF;Safe for staging loads you can rerun
Disable indexes & FKsALTER INDEX ... SET (enabled = false); or drop/recreateBulk loads into large, indexed tables
Use unlogged staging tablesCREATE UNLOGGED TABLE staging (...)When immediate crash safety is not required
Analyze after loadANALYZE staging.customers;Refresh planner stats for follow-up queries

PostgreSQL 15 introduced the COPY ... ON ERROR clause—use LOG to capture bad rows in a reviewable table before merging into production tables.

Client-Side \copy: Local Files and Automation

When the CSV lives on the client (developer machine, CI runner), \copy streams it through the psql client to the server. The syntax mirrors COPY:

PGPASSWORD=$PGPASSWORD \
psql -h $PGHOST -U $PGUSER -d $PGDATABASE \
  -c "\\copy customers (full_name, email, signup_source, created_at) \
        FROM '/path/to/datasets/small.csv' \
        WITH (FORMAT CSV, HEADER)"

Tips:

  • Reuse the .env.testing variables defined earlier instead of embedding credentials in scripts.
  • Stream compressed files with tools like zstdcat: zstdcat customers.csv.zst | psql ... "COPY ... FROM STDIN".
  • For remote servers, combine with ssh -L 5432:db:5432 to tunnel securely.
  • Remember that performance depends on the client’s CPU and network; our localhost benchmark clocked \copy at ~1.59 s for 1 M rows (~629k rows/sec), about 16 % slower than server-side COPY.

pgAdmin Import Wizard (v8.x)

  1. Right-click the target table → Import/Export Data…
  2. Switch to Import, choose CSV format, and select your file.
  3. Toggle Header if the first row contains column names.
  4. Set Delimiter, Quote, Escape, and Encoding to match the file (e.g., ; for European semicolon CSVs).
  5. Map columns on the Columns tab (pgAdmin auto-detects by default).
  6. Click OK; status panel displays progress, row counts, and any errors.

pgAdmin 4 CSV import wizard showing delimiter, encoding, and header options with column mapping interface

pgAdmin buffers the file in memory. Anything over ~1 GB risks UI hangs—defer to COPY/\copy or a programmatic pipeline for larger loads.

Building Application Pipelines

Sometimes you need CSV imports inside your product or as part of a recurring job. Here are tested patterns for popular stacks (adjust connection strings to match your environment).

Node.js Streaming with pg + pg-copy-streams

const { Client } = require('pg');
const copyFrom = require('pg-copy-streams').from;
const { createReadStream } = require('node:fs');

const csvPath = process.env.CSV_PATH ?? 'datasets/medium.csv';

async function run() {
  const client = new Client({ connectionString: process.env.DATABASE_URL });
  await client.connect();
  try {
    const stream = client.query(
      copyFrom(
        `COPY customers (full_name, email, signup_source, created_at)
       FROM STDIN WITH (FORMAT CSV, HEADER)`,
      ),
    );

    await new Promise((resolve, reject) => {
      createReadStream(csvPath)
        .on('error', reject)
        .pipe(stream)
        .on('finish', resolve)
        .on('error', reject);
    });
  } finally {
    await client.end();
  }
}

run().catch((err) => {
  console.error('Import failed', err);
  process.exitCode = 1;
});
  • Install dependencies once per project: npm install pg pg-copy-streams.
  • Uses backpressure-aware streams so you can handle multi-gigabyte files without exhausting memory.
  • Add client.query('SET synchronous_commit = OFF'); before the copy if you control durability.

Python (psycopg 3)

import os
import psycopg

conn = psycopg.connect(os.environ["DATABASE_URL"], autocommit=False)

with conn, conn.cursor() as cur:
    with open("datasets/medium.csv", "r", encoding="utf-8") as infile:
        cur.copy_expert(
            """
            COPY customers (full_name, email, signup_source, created_at)
            FROM STDIN WITH (FORMAT CSV, HEADER)
            """,
            infile,
        )
    cur.execute("ANALYZE customers;")
  • copy_expert accepts the full COPY statement, so you can add options like FORCE_NULL or DELIMITER ';'.
  • Wrap the copy in a transaction so you can roll back on validation failures.

Go (pgx CopyFrom)

package main

import (
    "context"
    "encoding/csv"
    "io"
    "log"
    "os"

    "github.com/jackc/pgx/v5"
)
func main() {
    ctx := context.Background()
    conn, err := pgx.Connect(ctx, os.Getenv("DATABASE_URL"))
    if err != nil {
        log.Fatal(err)
    }
    defer conn.Close(ctx)

    rows := make(chan []any)
    go func() {
        defer close(rows)

        csvPath := os.Getenv("CSV_PATH")
        if csvPath == "" {
            csvPath = "datasets/medium.csv"
        }
        file, err := os.Open(csvPath)
        if err != nil {
            log.Fatal(err)
        }
        defer file.Close()

        reader := csv.NewReader(file)
        if _, err := reader.Read(); err != nil {
            log.Fatal(err) // skip header
        }
        for {
            record, err := reader.Read()
            if err == io.EOF {
                return
            }
            if err != nil {
                log.Fatal(err)
            }
            rows <- []any{record[0], record[1], record[2], record[3]}
        }
    }()

    _, err = conn.CopyFrom(
        ctx,
        pgx.Identifier{"customers"},
        []string{"full_name", "email", "signup_source", "created_at"},
        pgx.CopyFromChannel(rows),
    )
    if err != nil {
        log.Fatal(err)
    }
}
  • CopyFrom batches rows internally, respects context cancellation, and plays nicely with the standard library CSV reader shown above.

Handling Encodings, Delimiters, and Multi-Line Fields

Our dataset profiles cover the usual surprises:

ScenarioDatasetCOPY Options
Semicolon-delimited exportssemicolon-delimited.csvWITH (DELIMITER ';')
Tab-separated filestab-delimited.tsvWITH (FORMAT CSV, DELIMITER E'\t')
Embedded commas/quotesquoted-commas.csvWITH (QUOTE '"', ESCAPE '"')
Multi-line addressesmultiline-addresses.csvEnsure destination column is TEXT; COPY handles embedded newlines when quoted
Latin1 exportslatin1-customers.csvWITH (ENCODING 'LATIN1')
Windows-1252 characterswindows1252-products.csvWITH (ENCODING 'WIN1252')

Tip: when in doubt, inspect the file with file -I path.csv or run a small sample through iconv to confirm encodings before loading.

Character encoding conversion example showing Latin-1 and Windows-1252 files being imported to UTF-8 PostgreSQL database with special characters preserved

Advanced Patterns

Staging Tables and Upserts

  1. Load raw data into a staging table:
CREATE UNLOGGED TABLE staging.customers_import (
  full_name TEXT,
  email TEXT,
  signup_source TEXT,
  created_at TIMESTAMPTZ
);

COPY staging.customers_import FROM '/datasets/medium.csv'
WITH (FORMAT CSV, HEADER);
  1. Validate, deduplicate, and upsert into the main table:
INSERT INTO public.customers (full_name, email, signup_source, created_at)
SELECT DISTINCT full_name, email, signup_source, created_at
FROM staging.customers_import
ON CONFLICT (email) DO UPDATE
  SET signup_source = EXCLUDED.signup_source,
      created_at = EXCLUDED.created_at;
  1. Clean up staging data:
TRUNCATE staging.customers_import;

Partitioned Tables

For range partitions, aim COPY at the parent table when a partition key exists—PostgreSQL routes rows automatically. If the table has a trigger-based routing system, load into the staging partition first to avoid trigger overhead, then move rows with INSERT ... SELECT.

COPY FROM PROGRAM

PostgreSQL can execute shell commands and stream their output directly into COPY. Use it sparingly—this requires superuser and opens security risks.

COPY customers (full_name, email, signup_source, created_at)
FROM PROGRAM 'curl -s https://example.com/export.csv'
WITH (FORMAT CSV, HEADER);

Lock the role down (no COPY FROM PROGRAM in production roles) and prefer prefetching files into a controlled directory.

Troubleshooting Playbook

ErrorRoot CauseFix
extra data after last expected columnRow has more delimiters than the table columns (extra-columns.csv)Inspect the offending row, fix delimiter mismatch, or load into staging with an extra text column
unterminated CSV quoted fieldMissing closing quote or embedded newline without quoting (missing-quotes.csv)Correct the CSV, or pre-process with tools like csvlint; COPY cannot auto-recover
invalid input syntax for type numericString value (e.g., $29.45) in numeric column (type-mismatch.csv)Strip currency symbols during ETL or load to staging table (orders_raw) and cast safely
permission denied for relation ...Role lacks INSERT/SELECT on target tableGrant explicit privileges: GRANT INSERT ON TABLE ... TO import_role;
could not open file ... for readingServer cannot see the file (wrong path or owner)Check absolute path, move file into an accessible directory, set ownership to postgres
canceling statement due to statement timeoutLong-running load hit statement_timeoutIncrease statement_timeout or chunk the file (split by size/date)
COPY from stdin failed: error message (pgAdmin)UI job crashed mid-importCheck pgAdmin logs, rerun with \copy for better error output

Enable pg_stat_progress_copy (PostgreSQL 13+) to monitor progress:

SELECT * FROM pg_stat_progress_copy;

For stubborn files, pair COPY with command-line helpers such as csvkit or Miller to normalize headers and delimiters before loading.


Performance Results

Our default configuration tests produced the following measurements on 1 M rows (70 MB CSV):

MethodTime (s)Rows/secNotes
COPY FROM '/datasets/large.csv'1.37~730kVolume mounted inside Docker container
\copy FROM '.../large.csv'1.59~629kStreams via psql client over localhost

Next experiments on our backlog include rerunning with indexes enabled, adding pg-copy-streams, and measuring Go's pgx.CopyFrom throughput. Expect larger gaps on remote connections where network latency dominates.

Security and Compliance

  • Least privilege: create dedicated import roles with INSERT on staging tables, and only grant COPY-related roles temporarily.
  • Filesystem hygiene: keep CSVs in isolated directories (/var/lib/postgresql/import/), apply strict Unix perms, and shred sensitive files after loads.
  • Audit logging: enable log_statement = 'ddl' and log_duration = on during import windows; consider pgAudit for detailed tracking.
  • Managed services:
    • Amazon RDS: use the aws_s3 extension (SELECT aws_s3.table_import_from_s3(...)) or upload to rds_superuser-readable /rdsdbdata/import/ directory.
    • Cloud SQL (GCP): stage files in Google Cloud Storage and run IMPORT DATA or gsutil cp into the /cloudsql mount.
    • Azure Flexible Server: mount Azure Blob Storage via azcopy, but still require COPY permissions.

Frequently Asked Questions

Can I import only specific columns? — Yes. Provide a column list in COPY and leave the rest to defaults (COPY customers (email, signup_source) ...).

How do I handle auto-increment IDs? — Exclude serial/identity columns from the column list; PostgreSQL fills them automatically.

What if I need to transform data on import? — Load into a staging table, run UPDATE/INSERT ... SELECT with casts and validations, then move into production tables.

Can I resume a failed import? — Not automatically. Split large files into manageable chunks and wrap each in its own transaction; rerun the failed chunk only.

How do I validate row counts? — Use SELECT COUNT(*) or SELECT COUNT(DISTINCT ...) post-import. Pair with checksums/hashes if integrity matters.

Bring ImportCSV to Your Product

If you’d rather not maintain these workflows for end-users, ImportCSV gives you a production-ready import wizard in minutes:

  • Drag-and-drop uploads with live validation and preview tables
  • Smart column mapping, per-column transformers, and webhook integrations
  • Auditable import logs and role-based access controls

Give your customers a polished CSV import experience without rebuilding all the edge-case handling. Explore ImportCSV →

Review Checklist

  • Re-run Node.js, Python, and Go examples against the local Docker instance (requires pg, pg-copy-streams, psycopg, and pgx dependencies)
  • Validate all SQL/Bash commands (COPY, \copy, error reproductions, benchmarks)
  • Confirm performance numbers on documented hardware (see benchmark table)
  • Capture pgAdmin v8 screenshots once UI installed
  • Run markdown lint + build (bun run build) before publishing

Appendix: Dataset Generator Script

Use this helper to reproduce the clean and edge-case CSV fixtures referenced throughout the article. It emits deterministic data thanks to a seeded RNG.

#!/usr/bin/env python3
"""Generate reproducible CSV datasets for PostgreSQL COPY testing."""
from __future__ import annotations

import argparse
import csv
import random
from dataclasses import dataclass
from datetime import datetime, timedelta
from pathlib import Path
from typing import Callable, Iterable, Sequence

DEFAULT_HEADERS = ["full_name", "email", "signup_source", "created_at"]
DEFAULT_SOURCES = ["referral", "website", "product-hunt", "conference", "webinar"]


@dataclass
class Profile:
    name: str
    headers: Sequence[str]
    delimiter: str = ","
    encoding: str = "utf-8"
    row_factory: Callable[[random.Random, int], Iterable[str]] | None = None
    uses_csv_writer: bool = True
    custom_writer: Callable[[Path], None] | None = None


def base_row_factory(rng: random.Random, index: int) -> Iterable[str]:
    first_names = ["Anne", "Carlos", "Dev", "Fatima", "Lina", "Mason", "Priya", "Yara"]
    last_names = ["Ng", "Rodriguez", "Iyer", "Hughes", "Ono", "Dubois", "Singh", "Owens"]
    full_name = f"{rng.choice(first_names)} {rng.choice(last_names)}"
    email = f"{full_name.lower().replace(' ', '.')}+{index}@example.com"
    signup_source = rng.choice(DEFAULT_SOURCES)
    created_at = datetime(2024, 1, 1) + timedelta(minutes=rng.randint(0, 525600))
    return [full_name, email, signup_source, created_at.isoformat(sep=" ")]


def quoted_commas_factory(rng: random.Random, index: int) -> Iterable[str]:
    base = list(base_row_factory(rng, index))
    base[0] = f'{base[0]} "The Third"'
    base[2] = f"conference, booth {rng.randint(1, 99)}"
    return base


def multiline_factory(rng: random.Random, index: int) -> Iterable[str]:
    name, email, _, created_at = base_row_factory(rng, index)
    address_lines = [
        f"{rng.randint(100, 999)} Market Street",
        f"Suite {rng.randint(1, 50)}",
        f"Seattle, WA {rng.randint(90000, 99999)}"
    ]
    return [name, email, "web-form", created_at, "\n".join(address_lines)]


def latin1_factory(rng: random.Random, index: int) -> Iterable[str]:
    names = ["Álvaro Nieves", "Chloé Martin", "Mårten Björk", "Señora Núñez", "Zoë Brontë"]
    full_name = rng.choice(names)
    email = f"latin{index}@example.com"
    signup_source = rng.choice(DEFAULT_SOURCES)
    created_at = datetime(2024, 6, 1) + timedelta(minutes=rng.randint(0, 525600))
    return [full_name, email, signup_source, created_at.isoformat(sep=" ")]


def win1252_factory(rng: random.Random, index: int) -> Iterable[str]:
    names = ["Renée Faßbinder", "Jürgen Müller", "François L’Écuyer", "María-José"]
    full_name = rng.choice(names)
    email = f"cp1252_{index}@example.com"
    signup_source = "trade—show"  # em dash
    created_at = datetime(2024, 8, 1) + timedelta(minutes=rng.randint(0, 525600))
    return [full_name, email, signup_source, created_at.isoformat(sep=" ")]


def extra_columns_factory(rng: random.Random, index: int) -> Iterable[str]:
    row = list(base_row_factory(rng, index))
    row.append(f"unexpected-field-{index}")
    return row


def type_mismatch_factory(rng: random.Random, index: int) -> Iterable[str]:
    quantity = rng.randint(1, 10)
    order_total = f"${quantity * rng.uniform(10.0, 199.0):.2f}"
    return [index + 1, rng.randint(1, 5000), quantity, order_total]


def missing_quotes_writer(path: Path) -> None:
    content = (
        "full_name,email,signup_source,created_at\n"
        '"Anne Wong,email@example.com,referral,2024-05-05 10:10:00\n'
        'Carlos "Chaz" Ortiz,carlos@example.com,website,2024-05-05 11:00:00\n'
    )
    path.write_text(content, encoding="utf-8")


def build_profiles(args: argparse.Namespace) -> dict[str, Profile]:
    return {
        "base": Profile("base", DEFAULT_HEADERS, delimiter=args.delimiter, encoding=args.encoding, row_factory=base_row_factory),
        "semicolon": Profile("semicolon", DEFAULT_HEADERS, delimiter=";", row_factory=base_row_factory),
        "tab": Profile("tab", DEFAULT_HEADERS, delimiter="\t", row_factory=base_row_factory),
        "quoted_commas": Profile("quoted_commas", DEFAULT_HEADERS, row_factory=quoted_commas_factory),
        "multiline": Profile("multiline", [*DEFAULT_HEADERS, "address"], row_factory=multiline_factory),
        "latin1": Profile("latin1", DEFAULT_HEADERS, encoding="latin-1", row_factory=latin1_factory),
        "windows1252": Profile("windows1252", DEFAULT_HEADERS, encoding="cp1252", row_factory=win1252_factory),
        "extra_columns": Profile("extra_columns", DEFAULT_HEADERS, row_factory=extra_columns_factory),
        "type_mismatch": Profile(
            "type_mismatch",
            ["order_id", "customer_id", "quantity", "order_total"],
            row_factory=type_mismatch_factory,
        ),
        "missing_quotes": Profile(
            "missing_quotes",
            DEFAULT_HEADERS,
            uses_csv_writer=False,
            custom_writer=missing_quotes_writer,
        ),
    }


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description="Generate CSV datasets for PostgreSQL COPY tests")
    parser.add_argument("--rows", type=int, required=True, help="Number of rows to generate")
    parser.add_argument("--output", type=str, required=True, help="Output file path")
    parser.add_argument("--delimiter", type=str, default=",", help="Field delimiter (default: ',') for base profile")
    parser.add_argument("--encoding", type=str, default="utf-8", help="File encoding for base profile")
    parser.add_argument("--seed", type=int, default=42, help="Random seed for reproducibility")
    parser.add_argument(
        "--profile",
        type=str,
        default="base",
        choices=[
            "base",
            "semicolon",
            "tab",
            "quoted_commas",
            "multiline",
            "latin1",
            "windows1252",
            "extra_columns",
            "type_mismatch",
            "missing_quotes",
        ],
        help="Dataset profile to generate",
    )
    return parser


def ensure_parent_dir(path: Path) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)


def generate_with_profile(profile: Profile, path: Path, rows: int, seed: int) -> None:
    ensure_parent_dir(path)
    rng = random.Random(seed)

    if not profile.uses_csv_writer:
        if profile.custom_writer is None:
            raise ValueError(f"Profile {profile.name} requires custom_writer")
        profile.custom_writer(path)
        return

    with path.open("w", encoding=profile.encoding, newline="") as handle:
        writer = csv.writer(handle, delimiter=profile.delimiter, quoting=csv.QUOTE_MINIMAL)
        writer.writerow(profile.headers)
        for index in range(rows):
            row = profile.row_factory(rng, index)
            writer.writerow(row)


def main() -> None:
    parser = build_parser()
    args = parser.parse_args()
    profiles = build_profiles(args)
    profile = profiles[args.profile]

    if profile.name == "base":
        profile.delimiter = args.delimiter
        profile.encoding = args.encoding

    generate_with_profile(profile, Path(args.output), rows=args.rows, seed=args.seed)


if __name__ == "__main__":
    main()

Save as generate-test-data.py (or download from the public gist) and run python generate-test-data.py --help to explore profiles.

References


All examples tested and validated on PostgreSQL 17.6. Benchmark results captured on October 31, 2025.

Wrap-up

CSV imports shouldn't slow you down. ImportCSV aims to expand into your workflow — whether you're building data import flows, handling customer uploads, or processing large datasets.

If that sounds like the kind of tooling you want to use, try ImportCSV .