Iceberg table format

dlt supports writing Iceberg tables when using the filesystem destination.

How it works

dlt uses the PyIceberg library to write Iceberg tables. One or multiple Parquet files are prepared during the extract and normalize steps. In the load step, these Parquet files are exposed as an Arrow data structure and fed into pyiceberg.

Iceberg catalogs support

dlt leverages pyiceberg's load_catalog function to be able to work with the same catalogs that pyiceberg would support, including REST and SQL catalogs. This includes using single-table, ephemeral, in-memory, SQLite-based catalogs. For more information on how pyiceberg works with catalogs, reference their documentation. To enable this, dlt either translates the configuration in the secrets.toml and config.toml into a valid pyiceberg config, or it delegates pyiceberg the task of resolving the needed configuration.

Iceberg dependencies

You need Python version 3.9 or higher and the pyiceberg package to use this format:

pip install "dlt[pyiceberg]"

You also need sqlalchemy>=2.0.18:

pip install 'sqlalchemy>=2.0.18'

Additional permissions for Iceberg

When using Iceberg with object stores like S3, additional permissions may be required for operations like multipart uploads and tagging. Make sure your IAM role or user has the following permissions:

[
  "s3:ListBucketMultipartUploads",
  "s3:GetBucketLocation",
  "s3:AbortMultipartUpload",
  "s3:PutObjectTagging",
  "s3:GetObjectTagging"
]

Set the config for your catalog and, if required, your storage config.

This is applicable only if you already have a catalog set-up. If you want to use ephemeral catalogs, you can skip this section, dlt will default to it.

dlt allows you to either provide pyiceberg's config through dlt's config mechanisms (secrets.toml or environment variables), or through the pyiceberg mechanisms supported by load_catalog (i.e. .pyiceberg.yaml or pyiceberg's env vars). To better learn how to set up .pyiceberg.yaml or to learn how pyiceberg utilizes env vars, please visit their documentation here. In the remaining of this doc snippet we will cover how to set it up through dlt's config mechanisms.

In the back, dlt utilizes load_catalog, so we try to keep the config we will pass along, as close as possible to the pyiceberg one. Specifically, we want to provide a catalog name and catalog type (rest or sql), and we provide them under the iceberg_catalog section of the secrets.toml or the env vars. These two variables will be used either to detect the catalog we want to load (for example, you have a .pyiceberg.yaml with multiple catalogs), and to do some validations when required.

[iceberg_catalog]
iceberg_catalog_name = "default"
iceberg_catalog_type = "rest"

or

export ICEBERG_CATALOG__ICEBERG_CATALOG_NAME=default
export ICEBERG_CATALOG__ICEBERG_CATALOG_TYPE=rest

If we don't provide these variables they will default to iceberg_catalog_name = 'default' and iceberg_catalog_type = 'sql'.

On top of this we will always require to provide a catalog configuration, either through dlt or through pyiceberg, dlt attempts to load your catalog in the following priority order:

1. Explicit config from secrets.toml (highest priority) - If you provide iceberg_catalog.iceberg_catalog_config in your secrets.toml, dlt will use this configuration
2. PyIceberg's standard mechanisms - If no explicit config is found, dlt delegates to pyiceberg's load_catalog, which searches for .pyiceberg.yaml or PYICEBERG_CATALOG_* environment variables
3. Ephemeral SQLite catalog (fallback) - If no configuration is found, dlt creates an in-memory SQLite catalog for backward compatibility and creates the configuration

In some cases, we will want the storage configuration as well (for instance, if you are not using vended-credential and instead you are using remote-signing). Let's start with the catalog configuration, which we store under iceberg_catalog.iceberg_catalog_config:

[iceberg_catalog.iceberg_catalog_config]
uri = "http://localhost:8181/catalog" # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__URI
type = "rest" # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__TYPE
warehouse = "default" # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__WAREHOUSE

In this case we are using a rest catalog located in localhost:8181 and we look for the default warehouse. These configs are passed in the same way and names as pyiceberg expects, which makes it easier to pass new configuration even if it is not in this snippet.

So let's continue now with storage. In some cases your catalog and storage may be able to handle vended-credentials, in that case you don't need to include the storage credentials here as the vended-credentials process will allow the catalog to temporarily generate those credentials. However, if you use remote-signing we need to provide them, and our configuration would become something like this:

[iceberg_catalog.iceberg_catalog_config]
uri = "http://localhost:8181/catalog"                 # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__URI
type = "rest"                                         # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__TYPE
warehouse = "default"                                 # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__WAREHOUSE
header.X-Iceberg-Access-Delegation = "remote-signing" # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__HEADER_X_ICEBERG_ACCESS_DELEGATION
py-io-impl = "pyiceberg.io.fsspec.FsspecFileIO"       # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__PY_IO_IMPL
s3.endpoint = "https://awesome-s3-buckets.example"    # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__S3_ENDPOINT
s3.access-key-id = "x1"                               # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__S3_ACCESS_KEY_ID
s3.secret-access-key = "y1"                           # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__S3_SECRET_ACCESS_KEY
s3.region = "eu-fr-1"                                 # DLT_ICEBERG_CATALOG__ICEBERG_CATALOG_CONFIG__S3_REGION

That's it!

Set table format

Set the table_format argument to iceberg when defining your resource:

@dlt.resource(table_format="iceberg")
def my_iceberg_resource():
    ...

or when calling run on your pipeline:

pipeline.run(my_resource, table_format="iceberg")

note

dlt always uses Parquet as loader_file_format when using the iceberg table format. Any setting of loader_file_format is disregarded.

Partitioning

Apache Iceberg supports table partitioning to optimize query performance. There are two ways to configure partitioning:

1. Using the iceberg_adapter function - for advanced partitioning with transformations (year, month, day, hour, bucket, truncate)
2. Using column-level partition property - for simple identity partitioning

note

Iceberg uses hidden partioning.

warning

Partition evolution (changing partition columns after a table has been created) is not supported.

Using the iceberg_adapter

The iceberg_adapter function allows you to configure partitioning with various transformation functions.

Basic example

from datetime import date

import dlt
from dlt.destinations.adapters import iceberg_adapter, iceberg_partition

data_items = [
    {"id": 1, "category": "A", "created_at": date(2025, 1, 1)},
    {"id": 2, "category": "A", "created_at": date(2025, 1, 15)},
    {"id": 3, "category": "B", "created_at": date(2025, 2, 1)},
]

@dlt.resource(table_format="iceberg")
def events():
    yield data_items

# Partition by category and month of created_at
iceberg_adapter(
    events,
    partition=[
        "category",  # identity partition (shorthand)
        iceberg_partition.month("created_at"),
    ],
)

pipeline = dlt.pipeline("iceberg_example", destination="filesystem")
pipeline.run(events)

To use advanced partitioning, import both the adapter and the iceberg_partition helper:

from dlt.destinations.adapters import iceberg_adapter, iceberg_partition

Partition transformations

Iceberg supports several transformation functions for partitioning. Use the iceberg_partition helper to create partition specifications:

• iceberg_partition.identity(column_name): Partition by exact column values (this is the same as passing the column name as a string to the iceberg_adapter)
• iceberg_partition.year(column_name): Partition by year from a date column
• iceberg_partition.month(column_name): Partition by month from a date column
• iceberg_partition.day(column_name): Partition by day from a date column
• iceberg_partition.hour(column_name): Partition by hour from a timestamp column
• iceberg_partition.bucket(n, column_name): Partition by hashed value into n buckets
• iceberg_partition.truncate(length, column_name): Partition by truncated string value to length

Bucket partitioning

Distribute data across a fixed number of buckets using a hash function:

iceberg_adapter(
    resource,
    partition=[iceberg_partition.bucket(16, "user_id")],
)

Truncate partitioning

Partition string values by a fixed prefix length:

iceberg_adapter(
    resource,
    partition=[iceberg_partition.truncate(3, "category")],  # "ELECTRONICS" → "ELE"
)

Custom partition field names

Specify custom names for partition fields:

iceberg_adapter(
    resource,
    partition=[
        iceberg_partition.year("activity_time", "activity_year"),
        iceberg_partition.bucket(8, "user_id", "user_bucket"),
    ],
)

Using column-level partition property

For simple identity partitioning, you can use the partition column hint directly in the resource definition:

@dlt.resource(
    table_format="iceberg",
    columns={"region": {"partition": True}}
)
def my_iceberg_resource():
    yield [
        {"id": 1, "region": "US", "amount": 100},
        {"id": 2, "region": "EU", "amount": 200},
    ]

Multiple columns can be partitioned:

@dlt.resource(
    table_format="iceberg",
    columns={
        "region": {"partition": True},
        "category": {"partition": True},
    }
)
def multi_partition_data():
    ...

Table access helper functions

You can use the get_iceberg_tables helper function to access native table objects. These are pyiceberg Table objects.

from dlt.common.libs.pyiceberg import get_iceberg_tables

# get dictionary of Table objects
iceberg_tables = get_iceberg_tables(pipeline)

# execute operations on Table objects
# etc.

Google Cloud Storage authentication

Note that not all authentication methods are supported when using Iceberg on Google Cloud Storage:

• OAuth - ✅ Supported
• Service Account - ❌ Not supported
• Application Default Credentials - ❌ Not supported

note

The S3-compatible interface for Google Cloud Storage is not supported when using iceberg.

Iceberg Azure scheme

The az scheme is not supported when using the iceberg table format. Please use the abfss scheme. This is because pyiceberg, which dlt used under the hood, currently does not support az.

Table format merge support

The upsert merge strategy is supported for iceberg. This strategy requires that the input data contains no duplicate rows based on the key columns, and that the target table also does not contain duplicates on those keys.

warning

Until pyiceberg > 0.9.1 is released, upsert is executed in chunks of 1000 rows.

warning

Schema evolution (changing the set of columns) is not supported when using the upsert merge strategy with pyiceberg == 0.10.0

@dlt.resource(
    write_disposition={"disposition": "merge", "strategy": "upsert"},
    primary_key="my_primary_key",
    table_format="iceberg"
)
def my_upsert_resource():
    ...