arrowjet 0.3.0

High-performance bulk data movement for Redshift

Arrowjet

The fastest way to move data in and out of cloud databases.

Arrowjet wraps each database's native bulk path in a simple Python API — no boilerplate, no S3 scripts, no slow INSERT loops.

pip install arrowjet              # core (PostgreSQL COPY, BYOC Engine, CLI)
pip install arrowjet[redshift]    # + Redshift driver (arrowjet.connect())
pip install arrowjet[full]        # + Redshift + SQLAlchemy

Supported databases:

• PostgreSQL / Aurora PostgreSQL / RDS PostgreSQL — via COPY protocol
• Amazon Redshift — via COPY/UNLOAD through S3

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Why Arrowjet

Standard database drivers move data row-by-row. For large datasets, this is the bottleneck — not the database.

Arrowjet uses each database's native bulk path instead. There is no slow path.

PostgreSQL — Writes (COPY FROM STDIN)

Approach	1M rows	vs Arrowjet
executemany (batch 1000)	~16 min	850x slower
Multi-row VALUES (batch 1000)	8.4s	7.4x slower
Arrowjet	1.13s	baseline

PostgreSQL — Reads (COPY TO STDOUT)

Approach	1M rows	vs Arrowjet
cursor.fetchall()	1.00s	1.5x slower
Arrowjet	0.65s	baseline

Benchmarked on RDS PostgreSQL 16.6, EC2 same region.

Redshift — Writes (COPY via S3)

Approach	1M rows	vs Arrowjet
write_dataframe() INSERT	13.4 hours	14,523x slower
executemany (batch 5000)	27.1 hours	29,296x slower
Manual COPY	4.06s	1.22x slower
Arrowjet	3.33s	baseline

Redshift — Reads (UNLOAD via S3)

Approach	1M rows	10M rows
cursor.fetchall()	~11s	~105s
Arrowjet	~4s	~34s

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Quick Start — PostgreSQL

No S3 bucket, no IAM role, no staging config. Just a psycopg2 connection.

import arrowjet
import psycopg2

conn = psycopg2.connect(host="your-host", dbname="mydb", user="user", password="...")

engine = arrowjet.Engine(provider="postgresql")

# Bulk write — 850x faster than executemany
engine.write_dataframe(conn, my_dataframe, "target_table")

# Bulk read — 1.5x faster than cursor.fetchall()
result = engine.read_bulk(conn, "SELECT * FROM events")
df = result.to_pandas()

Works with any PostgreSQL: Aurora, RDS, self-hosted, Docker, Supabase, Neon.

Quick Start — Redshift

import arrowjet

conn = arrowjet.connect(
    host="your-cluster.region.redshift.amazonaws.com",
    database="dev",
    user="awsuser",
    password="...",
    staging_bucket="your-staging-bucket",
    staging_iam_role="arn:aws:iam::123456789:role/RedshiftS3Role",
    staging_region="us-east-1",
)

# Bulk read — UNLOAD → S3 → Parquet → Arrow
result = conn.read_bulk("SELECT * FROM events WHERE date > '2025-01-01'")
df = result.to_pandas()

# Bulk write — Arrow → Parquet → S3 → COPY
conn.write_dataframe(my_dataframe, "target_table")

# Safe mode — standard DBAPI for small queries
df = conn.fetch_dataframe("SELECT COUNT(*) FROM events")

---

Bring Your Own Connection

Already have connection management? Arrowjet works with your existing connections.

import arrowjet

# PostgreSQL — no staging config needed
pg_engine = arrowjet.Engine(provider="postgresql")
pg_engine.write_dataframe(existing_pg_conn, df, "my_table")
result = pg_engine.read_bulk(existing_pg_conn, "SELECT * FROM my_table")

# Redshift — needs S3 staging config
rs_engine = arrowjet.Engine(
    provider="redshift",
    staging_bucket="your-bucket",
    staging_iam_role="arn:aws:iam::123:role/RedshiftS3Role",
    staging_region="us-east-1",
)
rs_engine.write_dataframe(existing_rs_conn, df, "my_table")
result = rs_engine.read_bulk(existing_rs_conn, "SELECT * FROM my_table")

Works with psycopg2, psycopg3, redshift_connector, ADBC, or any DBAPI connection.

---

CLI

# PostgreSQL
arrowjet export --provider postgresql --query "SELECT * FROM users" --to ./users.parquet
arrowjet export --provider postgresql --query "SELECT * FROM users" --to ./users.csv --format csv

# Redshift
arrowjet export --query "SELECT * FROM sales" --to ./out.parquet
arrowjet export --query "SELECT * FROM sales" --to s3://bucket/sales/
arrowjet import --from s3://bucket/sales/ --to sales_table
arrowjet import --from ./data.parquet --to sales_table

# Inspect data
arrowjet preview --file ./out.parquet
arrowjet validate --table sales --row-count --schema --sample

# Configure profiles
arrowjet configure

All commands read connection details from ~/.arrowjet/config.yaml (set up with arrowjet configure). Override per-command with --host, --password, --profile, --provider, etc.

See docs/cli_reference.md for full details.

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Authentication

PostgreSQL

Password auth via psycopg2 connection parameters. IAM database auth for Aurora/RDS coming soon.

Redshift

Three methods supported:

• Password — default, standard credentials
• IAM — auth_type="iam", temporary credentials via GetClusterCredentials (provisioned) or GetCredentials (serverless)
• Secrets Manager — auth_type="secrets_manager", fetch credentials from a secret ARN

See docs/configuration.md for the full reference.

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Integrations

Airflow

@task
def export_from_postgres():
    import arrowjet, psycopg2
    conn = psycopg2.connect(host=..., dbname=..., ...)
    engine = arrowjet.Engine(provider="postgresql")
    result = engine.read_bulk(conn, "SELECT * FROM events")
    # write to S3, transform, etc.
    conn.close()

See examples/airflow/ for Redshift examples with before/after comparison.

dbt

bash examples/dbt/run_with_arrowjet.sh

See examples/dbt/ for the full setup.

SQLAlchemy (Redshift)

from sqlalchemy import create_engine
engine = create_engine("redshift+arrowjet://user:pass@host:5439/dev")

---

How It Works

Each database has a fast bulk path that most Python users don't know about:

Database	Slow path (what most people use)	Fast path (what arrowjet uses)
PostgreSQL	executemany(), to_sql()	COPY FROM STDIN / COPY TO STDOUT
Redshift	INSERT, write_dataframe()	COPY / UNLOAD via S3

Arrowjet wraps the fast path in a one-line API. There is no slow path.

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Requirements

• Python 3.10+
• PostgreSQL: psycopg2 or psycopg2-binary (any PostgreSQL — Aurora, RDS, self-hosted)
• Redshift: pip install arrowjet[redshift] + S3 bucket + IAM role

See docs/iam_setup.md for Redshift IAM configuration.

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Roadmap

• Redshift (COPY/UNLOAD via S3)
• PostgreSQL (COPY protocol)
• MySQL / Aurora MySQL (LOAD DATA)
• Snowflake (COPY INTO via stages)
• Data validation (row counts, schema checks, null detection)

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License

MIT