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Arrow Table / Pandas

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You can load data directly from an Arrow table or Pandas dataframe. This is supported by all destinations, but recommended especially when using destinations that support the parquet foramt 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.


To write an Arrow source, pass any pyarrow.Table, pyarrow.RecordBatch or pandas.DataFrame object (or list of 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"), table_name="orders")

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), table_name="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 or pandas frames to any destination but performance will vary.

Destinations that support parquet natively for direct loading

  • duckdb & motherduck
  • redshift
  • bigquery
  • snowflake
  • filesystem
  • athena

Normalize configuration

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:

add_dlt_load_id = true
add_dlt_id = true

Keep in mind that enabling these incurs some performance overhead because the parquet 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 as well. Usage is the same as without other dlt resources. Refer to the incremental loading guide for more information.


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

# Create a resource using that yields a dataframe, using the `ordered_at` field as an incremental cursor
def orders(ordered_at = dlt.sources.incremental('ordered_at'))
# Get 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")
# Run again to load only new data

Look at the Connector X + Arrow Example to see how to load data from production databases fast.

Supported Arrow data types

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

Arrow typedlt typeNotes
timestamptimestampPrecision is determined by the unit of the timestamp.
time<bit_width>timePrecision is determined by the unit of the time.
int<bit_width>bigintPrecision is determined by the bit width.
decimaldecimalPrecision and scale are determined by the type properties.

Loading nested types

All struct types are represented as complex and will be loaded as JSON (if destination permits) or a string. Currently we do not support struct types, even if they are present in the destination.

If you want to represent nested data as separated tables, you must yield panda frames and arrow tables as records. In the examples above:

# yield panda frame as records'records'), table_name="orders")

# yield arrow table, table_name="orders")

Both Pandas and Arrow allow to stream records in batches.

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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