Delta

dltHub

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The Delta destination is based on the filesystem destination in dlt. All configuration options from the filesystem destination can be configured as well.

warning

Under the hood, dltHub uses the deltalake library to write Delta tables. Beware that when loading a large amount of data for one table, the underlying Rust implementation will consume a lot of memory. This is a known issue, and the maintainers are actively working on a solution. You can track the progress here. Until the issue is resolved, you can mitigate the memory consumption by doing multiple smaller incremental pipeline runs.

Setup

Make sure you have installed the necessary dependencies:

pip install deltalake
pip install pyarrow>=2.0.18

Initialize a dltHub project in the current working directory with the following command:

# replace sql_database with the source of your choice
dlt project init sql_database delta

This will create a Delta destination in your dlt.yml, where you can configure the destination:

destinations:
  delta_destination:
    type: delta
    bucket_url: "s3://your_bucket" # replace with bucket url

The credentials can be defined in the secrets.toml:

AWS S3

# secrets.toml
[destination.delta.credentials]
aws_access_key_id="Please set me up!"
aws_secret_access_key="Please set me up!"

GCS/GDrive

# secrets.toml
[destination.delta.credentials]
azure_storage_account_name="Please set me up!"
azure_storage_account_key="Please set me up!"

Azure

warning

Only Service Account and Application Default Credentials authentication methods are supported for Google Cloud Storage.

# secrets.toml
[destination.delta.credentials]
client_email="Please set me up!"
private_key="Please set me up!"
project_id="Please set me up!"

SFTP

Learn how to set up SFTP credentials for each authentication method in the SFTP section. For example, in the case of key-based authentication, you can configure the source the following way:

# secrets.toml
[destination.delta.credentials]
sftp_username = "foo"
sftp_key_filename = "/path/to/id_rsa"     # Replace with the path to your private key file
sftp_key_passphrase = "your_passphrase"   # Optional: passphrase for your private key

The Delta destination can also be defined in Python as follows:

pipeline = dlt.pipeline("loads_delta", destination="delta")

Write dispositions

The Delta destination handles the write dispositions as follows:

• append - files belonging to such tables are added to the dataset folder.
• replace - all files that belong to such tables are deleted from the dataset folder, and then the current set of files is added.
• merge - can be used only with the upsert merge strategy.

warning

The upsert merge strategy for the Delta destination is experimental.

The merge write disposition can be configured as follows on the source/resource level:

dlt.yml

sources:
  my_source:
    type: sources.my_source
    with_args:
      write_disposition:
        disposition: merge
        strategy: upsert

Python

@dlt.resource(
    primary_key="id",  # merge_key also works; primary_key and merge_key may be used together
    write_disposition={"disposition": "merge", "strategy": "upsert"},
)
def my_resource():
    yield [
        {"id": 1, "foo": "foo"},
        {"id": 2, "foo": "bar"}
    ]
...

pipeline = dlt.pipeline("loads_delta", destination="delta")

Or on the pipeline.run level:

pipeline.run(write_disposition={"disposition": "merge", "strategy": "upsert"})

Partitioning

Delta tables can be partitioned (using Hive-style partitioning) by specifying one or more partition column hints on the source/resource level:

dlt.yml

sources:
  my_source:
    type: sources.my_source
    with_args:
      columns:
        foo:
          partition: True

Python

@dlt.resource(
  columns={"_dlt_load_id": {"partition": True}}
)
def my_resource():
    ...

pipeline = dlt.pipeline("loads_delta", destination="delta")

warning

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

Table access helper functions

You can use the get_delta_tables helper functions to access the native DeltaTable objects.

from dlt.common.libs.deltalake import get_delta_tables

...

# get dictionary of DeltaTable objects
delta_tables = get_delta_tables(pipeline)

# execute operations on DeltaTable objects
delta_tables["my_delta_table"].optimize.compact()
delta_tables["another_delta_table"].optimize.z_order(["col_a", "col_b"])
# delta_tables["my_delta_table"].vacuum()
# etc.

Table format

The Delta destination automatically assigns the delta table format to all resources that it will load. You can still fall back to storing files by setting table_format to native on the resource level:

@dlt.resource(
  table_format="native"
)
def my_resource():
    ...

pipeline = dlt.pipeline("loads_delta", destination="delta")

Storage options and configuration

You can pass storage options and configuration by configuring both destination.filesystem.deltalake_storage_options and destination.filesystem.deltalake_configuration:

[destination.filesystem]
deltalake_configuration = '{"delta.enableChangeDataFeed": "true", "delta.minWriterVersion": "7"}'
deltalake_storage_options = '{"AWS_S3_LOCKING_PROVIDER": "dynamodb", "DELTA_DYNAMO_TABLE_NAME": "custom_table_name"}'

dlt passes these as arguments to the write_deltalake method in the deltalake library (deltalake_configuration maps to configuration and deltalake_storage_options maps to storage_options). Look at their documentation to see which options can be used.

You don't need to specify credentials here. dlt merges the required credentials with the options you provided before passing it as storage_options.

warning

When using s3, you need to specify storage options to configure locking behavior.