Great question! In Prefect 3.x, you can use the

SqlAlchemyConnector

from the

prefect-sqlalchemy

collection to provide a SQLAlchemy Engine/Connection to pandas, then call

DataFrame.to_sql(...)

. Here’s a minimal, working pattern. 1) Install dependencies

pip install "prefect[sqlalchemy]" pandas

2) (Optional) Create and save a connector block

from prefect_sqlalchemy.database import SqlAlchemyConnector

# Example for Postgres with psycopg3
connector = SqlAlchemyConnector(
    connection_info="<postgresql+psycopg://user:password@host:5432/dbname>",
    # connect_args={"sslmode": "require"}  # optional
)
connector.save("my-db", overwrite=True)

3) Write a pandas DataFrame using the connector (outside or inside a Prefect flow)

import pandas as pd
from prefect_sqlalchemy.database import SqlAlchemyConnector

df = pd.DataFrame(
    [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]
)

# Load a saved connector, or construct one inline (see step 2)
connector = SqlAlchemyConnector.load("my-db")

# Option A: Use an Engine
engine = connector.get_engine()
with engine.begin() as conn:  # ensures commit/rollback
    df.to_sql(
        name="my_table",
        con=conn,
        schema="public",         # optional; depends on your DB
        if_exists="append",      # or "replace"/"fail"
        index=False,
        method="multi",          # batches INSERTs
        chunksize=1000           # good for large DataFrames
    )

# Option B: Use a Connection directly
# with connector.get_connection() as conn:
#     df.to_sql("my_table", con=conn, if_exists="append", index=False, method="multi", chunksize=1000)

Using within a Prefect flow

from prefect import flow
import pandas as pd
from prefect_sqlalchemy.database import SqlAlchemyConnector

@flow
def load_dataframe():
    df = pd.DataFrame([{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}])
    connector = SqlAlchemyConnector.load("my-db")
    with connector.get_connection() as conn:
        df.to_sql(
            "my_table",
            con=conn,
            if_exists="append",
            index=False,
            method="multi",
            chunksize=1000,
        )

if __name__ == "__main__":
    load_dataframe()

Notes and tips - Pandas

to_sql

expects a synchronous SQLAlchemy connectable. If you’re using an async driver (e.g.,

postgresql+asyncpg

), switch to a sync URL (e.g.,

postgresql+psycopg

) for

to_sql

. - You can control column types with SQLAlchemy types:

from sqlalchemy import types as satypes
  df.to_sql("my_table", con=conn, if_exists="append", index=False, dtype={"name": satypes.String(255)})

- For large loads,

method="multi"

and

chunksize

help performance. For Postgres, drivers like psycopg3 typically perform well. - If the table doesn’t exist,

to_sql

can create it when

if_exists="append"

(unless your DB/schema permissions prevent it). Helpful docs - Prefect SQLAlchemy collection: prefect-sqlalchemy docs - Pandas `to_sql`: DataFrame.to_sql - SQLAlchemy database URLs: SQLAlchemy database URLs If you can share which database/driver you’re using and roughly how large your DataFrame is, I can tailor the example (e.g., best driver options, dtype mapping, and performance tweaks).