import Header from './_source-info-header.md';

Arrow table / Pandas / Polars#

You can load data directly from an Arrow table, Pandas dataframe, or Polars DataFrame.
This is supported by all destinations, but it is especially recommended when using destinations that support the Parquet file format natively (e.g., Snowflake and Filesystem).
See the destination support section for more information.

When used with a Parquet supported destination, this is a more performant way to load structured data since dlt bypasses many processing steps normally involved in passing JSON objects through the pipeline.
dlt automatically translates the Arrow table's schema to the destination table's schema and writes the table to a parquet file, which gets uploaded to the destination without any further processing.

Usage#

To write an Arrow source, pass any pyarrow.Table, pyarrow.RecordBatch, pandas.DataFrame, polars.DataFrame, or polars.LazyFrame object (or list thereof) to the pipeline's run or extract method, or yield table(s)/dataframe(s) from a @dlt.resource decorated function.

This example loads a Pandas dataframe to a Snowflake table:

import dlt
from dlt.common import pendulum
import pandas as pd

df = pd.DataFrame({
    "order_id": [1, 2, 3],
    "customer_id": [1, 2, 3],
    "ordered_at": [pendulum.DateTime(2021, 1, 1, 4, 5, 6), pendulum.DateTime(2021, 1, 3, 4, 5, 6), pendulum.DateTime(2021, 1, 6, 4, 5, 6)],
    "order_amount": [100.0, 200.0, 300.0],
})

pipeline = dlt.pipeline("orders_pipeline", destination="snowflake")

pipeline.run(df, table_name="orders")

Note that Pandas indexes are not saved by default (up from dlt version 1.4.1). If for some reason you need the destination,
use Table.from_pandas with preserve_index set to True to explicitly convert the dataframe into arrow table.

A pyarrow table can be loaded in the same way:

import pyarrow as pa

# Create dataframe and pipeline same as above
...

table = pa.Table.from_pandas(df)
pipeline.run(table, table_name="orders")

Polars#

You can yield Polars DataFrames and LazyFrames directly from a @dlt.resource. They are automatically converted to Arrow tables before extraction, so all Arrow-based optimizations (Parquet direct loading, schema translation, etc.) apply.

import dlt
import polars as pl

df = pl.DataFrame({
    "order_id": [1, 2, 3],
    "customer_id": [1, 2, 3],
    "order_amount": [100.0, 200.0, 300.0],
})

pipeline = dlt.pipeline("orders_pipeline", destination="duckdb")
pipeline.run(df, table_name="orders")

LazyFrames are collected automatically before conversion:

import dlt
import polars as pl

@dlt.resource
def orders():
    lf = pl.scan_csv("orders.csv")
    # filter lazily; dlt collects and converts to Arrow on extraction
    yield lf.filter(pl.col("amount") > 100)

pipeline = dlt.pipeline("orders_pipeline", destination="duckdb")
pipeline.run(orders)

Note: The data in the table must be compatible with the destination database as no data conversion is performed. Refer to the documentation of the destination for information about supported data types.

Destination support#

Destinations that support the Parquet format natively will have the data files uploaded directly as possible. Rewriting files can be avoided completely in many cases.

When the destination does not support Parquet, the rows are extracted from the table and written in the destination's native format (usually insert_values), and this is generally much slower
as it requires processing the table row by row and rewriting data to disk.

The output file format is chosen automatically based on the destination's capabilities, so you can load arrow, pandas, or polars frames to any destination, but performance will vary.

Destinations that support parquet natively for direct loading#

• duckdb & motherduck
• redshift
• bigquery
• snowflake
• filesystem
• athena
• databricks
• dremio
• synapse

Handling schema mismatches across batches#

When a resource yields multiple Arrow tables, DataFrames, or Polars DataFrames, dlt concatenates them before writing to disk. By default, all batches must have identical schemas — any type difference (e.g., int64 vs float64) raises an ArrowInvalid error. This is common when reading multiple source files where pandas infers slightly different types per file.

The arrow_concat_promote_options setting controls how type differences are resolved:

[sources.data_writer]
arrow_concat_promote_options = "permissive"

DATA_WRITER__ARROW_CONCAT_PROMOTE_OPTIONS=permissive

Add _dlt_load_id and _dlt_id to your tables#

dlt does not add any data lineage columns by default when loading Arrow tables. This is to give the best performance and avoid unnecessary data copying.

But if you need them, the _dlt_load_id (ID of the load operation when the row was added) and _dlt_id (unique ID for the row) columns can be added respectively with the following configuration options:

[normalize.parquet_normalizer]
add_dlt_load_id = true
add_dlt_id = true

Keep in mind that enabling these incurs some performance overhead:

• add_dlt_load_id has minimal overhead since the column is added to the arrow table in memory during the extract stage, before the parquet file is written to disk
• add_dlt_id adds the column during the normalize stage after the file has been extracted to disk. The file needs to be read back from disk in chunks, processed, and rewritten with new columns

Incremental loading with Arrow tables#

You can use incremental loading with Arrow tables, Pandas DataFrames, and Polars DataFrames.
Usage is the same as with other dlt resources. Refer to the incremental loading guide for more information.

Example:

import dlt
from dlt.common import pendulum
import pandas as pd

# Create a resource that yields a dataframe, using the `ordered_at` field as an incremental cursor
@dlt.resource(primary_key="order_id")
def orders(ordered_at = dlt.sources.incremental('ordered_at')):
    # Get a dataframe/arrow table from somewhere
    # If your database supports it, you can use the last_value to filter data at the source.
    # Otherwise, it will be filtered automatically after loading the data.
    df = _get_orders(since=ordered_at.last_value)
    yield df

pipeline = dlt.pipeline("orders_pipeline", destination="snowflake")
pipeline.run(orders)
# Run again to load only new data
pipeline.run(orders)

Loading JSON documents#

If you want to skip the default dlt JSON normalizer, you can use any available method to convert JSON documents into tabular data.

• pandas has read_json and json_normalize methods
• pyarrow can infer the table schema and convert JSON files into tables with read_json
• duckdb can do the same with read_json_auto

import duckdb

conn = duckdb.connect()
table = conn.execute("SELECT * FROM read_json_auto('./json_file_path')").fetch_arrow_table()

Note that duckdb and pyarrow methods will generate nested types for nested data, which are only partially supported by dlt.

Supported Arrow data types#

The Arrow data types are translated to dlt data types as follows:

Loading nested types#

All struct types are represented as json and will be loaded as JSON (if the destination permits) or a string. Currently, we do not support struct types,
even if they are present in the destination (except BigQuery which can be configured to handle them)

If you want to represent nested data as separate tables, you must yield DataFrames and Arrow tables as records. In the examples above:

# yield pandas DataFrame as records
pipeline.run(df.to_dict(orient='records'), table_name="orders")

# yield arrow table
pipeline.run(table.to_pylist(), table_name="orders")

# yield polars DataFrame as records
pipeline.run(df.to_dicts(), table_name="orders")

Pandas, Arrow and Polars all allow streaming records in batches.