Destination

Destination is a location in which dlt creates and maintains the current version of the schema and loads your data. Destinations come in various forms: databases, datalakes, vector stores, or files. dlt deals with this variety via destination type modules which you declare when creating a pipeline.

We maintain a set of built-in destinations that you can use right away.

Declare the destination type

We recommend that you declare the destination type when creating a pipeline instance with dlt.pipeline. This allows the run method to synchronize your local pipeline state with the destination and extract and normalize to create compatible load packages and schemas. You can also pass the destination to the run and load methods.

• Use destination shorthand type

import dlt

pipeline = dlt.pipeline("pipeline", destination="filesystem")

Above, we want to use the filesystem built-in destination. You can use shorthand types only for built-ins.

• Use a named destination with a configured type

import os
import dlt

os.environ["DESTINATION__MY_DESTINATION__DESTINATION_TYPE"] = "filesystem"

pipeline = dlt.pipeline("pipeline", destination="my_destination")

Above, we use a custom destination name and configure the destination type to filesystem using an environment variable. This approach is especially useful when switching between destinations without modifying the actual pipeline code. See details in the section on using named destinations.

• Use full destination factory type

import dlt

pipeline = dlt.pipeline("pipeline", destination="dlt.destinations.filesystem")

Above, we use the built-in filesystem destination by providing a factory type filesystem from the module dlt.destinations. You can implement your own destination and pass this external module as well.

• Import destination factory

import dlt
from dlt.destinations import filesystem

pipeline = dlt.pipeline("pipeline", destination=filesystem)

Above, we import the destination factory for filesystem and pass it to the pipeline.

All examples above will create the same destination class with default parameters and pull required config and secret values from configuration - they are equivalent.

Pass explicit parameters and a name to a destination factory

You can instantiate the destination factory yourself to configure it explicitly. When doing this, you work with destinations the same way you work with sources

import dlt

azure_bucket = filesystem("az://dlt-azure-bucket", destination_name="production_az_bucket")
pipeline = dlt.pipeline("pipeline", destination=azure_bucket)

Above, we import and instantiate the filesystem destination factory. We pass the explicit URL of the bucket and name the destination production_az_bucket.

If a destination is not named, its shorthand type (the Python factory name) serves as the destination name. Name your destination explicitly if you need several separate configurations for destinations of the same type (i.e., when you wish to maintain credentials for development, staging, and production storage buckets in the same config file). The destination name is also stored in the load info and pipeline traces, so use explicit names when you need more descriptive identifiers (rather than generic names like filesystem).

Configure a destination

We recommend passing the credentials and other required parameters to configuration via TOML files, environment variables, or other config providers. This allows you, for example, to easily switch to production destinations after deployment.

Use the default config section layout as shown below:

[destination.filesystem]
bucket_url="az://dlt-azure-bucket"
[destination.filesystem.credentials]
azure_storage_account_name="dltdata"
azure_storage_account_key="storage key"

Alternatively, you can use environment variables:

DESTINATION__FILESYSTEM__BUCKET_URL=az://dlt-azure-bucket
DESTINATION__FILESYSTEM__CREDENTIALS__AZURE_STORAGE_ACCOUNT_NAME=dltdata
DESTINATION__FILESYSTEM__CREDENTIALS__AZURE_STORAGE_ACCOUNT_KEY="storage key"

When using destination factories, use the destination name in the config section:

[destination.production_az_bucket]
bucket_url="az://dlt-azure-bucket"
[destination.production_az_bucket.credentials]
azure_storage_account_name="dltdata"
azure_storage_account_key="storage key"

For custom destination names passed to your pipeline (e.g., destination="my_destination"), dlt resolves the destination type from configuration. Add destination_type to specify which destination type to use:

[destination.my_destination]
destination_type="filesystem"
bucket_url="az://dlt-azure-bucket"
[destination.my_destination.credentials]
azure_storage_account_name="dltdata"
azure_storage_account_key="storage key"

Note that when you use the dlt init command to create or add a data source, dlt creates a sample configuration for the selected destination.

Pass explicit credentials

You can pass credentials explicitly when creating a destination factory instance. This replaces the credentials argument in dlt.pipeline and pipeline.load methods, which is now deprecated. You can pass the required credentials object, its dictionary representation, or the supported native form like below:

import dlt
from dlt.destinations import postgres

# pass full credentials - together with the password (not recommended)
pipeline = dlt.pipeline(
    "pipeline",
    destination=postgres(credentials="postgresql://loader:loader@localhost:5432/dlt_data"),
)

tip

You can create and pass partial credentials, and dlt will fill in the missing data. Below, we pass a PostgreSQL connection string but without a password and expect that it will be present in environment variables (or any other config provider)

import dlt
from dlt.destinations import postgres

# pass credentials without password
# dlt will retrieve the password from ie. DESTINATION__POSTGRES__CREDENTIALS__PASSWORD
prod_postgres = postgres(credentials="postgresql://loader@localhost:5432/dlt_data")
pipeline = dlt.pipeline("pipeline", destination=prod_postgres)

import dlt
from dlt.destinations import filesystem
from dlt.sources.credentials import AzureCredentials

credentials = AzureCredentials()
# fill only the account name, leave key to be taken from secrets
credentials.azure_storage_account_name = "production_storage"
pipeline = dlt.pipeline(
    "pipeline", destination=filesystem("az://dlt-azure-bucket", credentials=credentials)
)

Please read how to use various built-in credentials types.

Inspect destination capabilities

Destination capabilities tell dlt what a given destination can and cannot do. For example, it tells which file formats it can load, what the maximum query or identifier length is. Inspect destination capabilities as follows:

import dlt
pipeline = dlt.pipeline("snowflake_test", destination="snowflake")
print(dict(pipeline.destination.capabilities()))

Pass additional parameters and change destination capabilities

The destination factory accepts additional parameters that will be used to pre-configure it and change destination capabilities.

import dlt
duck_ = dlt.destinations.duckdb(naming_convention="duck_case", recommended_file_size=120000)
print(dict(duck_.capabilities()))

The example above is overriding the naming_convention and recommended_file_size in the destination capabilities.

Use named destinations

Named destinations are destinations with a custom name, the type of which can be explicitly provided or be resolved from the toml files or equivalent environment variables. There are multiple ways to use named destinations:

• Use a named destination string reference with type configured via an environment variable

import os
import dlt

os.environ["DESTINATION__MY_DESTINATION__DESTINATION_TYPE"] = "filesystem"

pipeline = dlt.pipeline("pipeline", destination="my_destination")

• Use dlt.destination() with the type configured via an environment variable

import os
import dlt

os.environ["DESTINATION__MY_DESTINATION__DESTINATION_TYPE"] = "filesystem"

pipeline = dlt.pipeline("pipeline", destination=dlt.destination("my_destination"))

• Use dlt.destination() with the type explicitly configured

import dlt

pipeline = dlt.pipeline(
    "pipeline", destination=dlt.destination("my_destination", destination_type="filesystem")
)

For all of the above, the destination type can alternatively be configured in the secrets.toml file as follows:

[destination.my_destination]
destination_type = "filesystem"

note

When resolving non-module destination string references (e.g., "bigquery" or "my_destination", not "dlt.destinations.bigquery"), dlt first attempts to resolve the reference as a named destination with a valid destination type configured, then falls back to shorthand type resolution.

This means that, in the examples above, if the destination type was not properly configured or was not a valid destination type, dlt would have attempted to resolve "my_destination" as a shorthand for a built-in type and would have eventually failed.

As another example, the following:

import os
import dlt

os.environ["DESTINATION__BIGQUERY__DESTINATION_TYPE"] = "duckdb"

pipeline = dlt.pipeline("pipeline", destination="bigquery")

will be resolved as a DuckDB destination that is named "bigquery", because a valid destination type "duckdb" is configured and dlt does not attempt to resolve the name "bigquery" as a shorthand for a built-in type!

Exception: If dlt.destination() is used and the destination_type is explicitly provided as an argument, dlt will skip the shorthand fallback and only attempt named destination resolution.

Configure multiple destinations of the same type

One of the benefits of named destinations is the ability to configure multiple destinations of the same type for use across different pipelines in a single script. For example, if you have two BigQuery destinations, you could define the following in the secrets.toml file:

[destination.my_destination]
location = "US"
[destination.my_destination.credentials]
project_id = "please set me up!"
private_key = "please set me up!"
client_email = "please set me up!"

[destination.my_other_destination]
location = "EU"
[destination.my_other_destination.credentials]
project_id = "please set me up!"
private_key = "please set me up!"
client_email = "please set me up!"

And use it in the pipeline code as follows:

import dlt

# Configure the pipeline to use the "my_destination" BigQuery destination
my_pipeline = dlt.pipeline(
    pipeline_name='my_pipeline',
    destination=dlt.destination("my_destination", destination_type="bigquery"),
    dataset_name='dataset_name'
)

# Configure the pipeline to use the "my_other_destination" BigQuery destination
my_other_pipeline = dlt.pipeline(
    pipeline_name='my_other_pipeline',
    destination=dlt.destination("my_other_destination", destination_type="bigquery"),
    dataset_name='dataset_name'
)

Use named destinations to switch destinations without changing code

Another advantage of named destinations is environment-based switching. When you need to use different destinations across development, staging, and production environments, named destinations allow you to switch destinations by modifying only the toml configuration files without changing the pipeline code. For example, if you are developing with DuckDB, you would first have your secrets.toml file configured as follows:

[destination.my_destination]
destination_type = "duckdb"

With the pipeline code being:

import dlt

pipeline = dlt.pipeline(
    pipeline_name='my_pipeline',
    destination='my_destination',
    dataset_name='dataset_name'
)

Then when you deploy the script, you would simply adjust your toml file with:

[destination.my_destination]
destination_type = "bigquery"
[destination.my_destination.credentials]
project_id = "please set me up!"
private_key = "please set me up!"
client_email = "please set me up!"

And keep the pipeline code intact.

Access a destination

When loading data, dlt will access the destination in two cases:

1. At the beginning of the run method to sync the pipeline state with the destination (or if you call pipeline.sync_destination explicitly).
2. In the pipeline.load method - to migrate the schema and load the load package.

dlt will also access the destination when you instantiate sql_client.

note

dlt will not import the destination dependencies or access destination configuration if access is not needed. You can build multi-stage pipelines where steps are executed in separate processes or containers - the extract and normalize step do not need destination dependencies, configuration, and actual connection.

import dlt
from dlt.destinations import filesystem

# just declare the destination.
pipeline = dlt.pipeline("pipeline", destination="filesystem")
# no destination credentials not config needed to extract
pipeline.extract(["a", "b", "c"], table_name="letters")
# same to normalize
pipeline.normalize()
# here dependencies dependencies will be imported, secrets pulled and destination accessed
# we pass bucket_url explicitly and expect credentials passed by config provider
load_info = pipeline.load(destination=filesystem(bucket_url=bucket_url))
print(load_info)

Control how dlt creates table, column, and other identifiers

dlt maps identifiers found in the source data into destination identifiers (i.e., table and column names) using naming conventions which ensure that character set, identifier length, and other properties fit into what the given destination can handle. For example, our default naming convention (snake case) converts all names in the source (i.e., JSON document fields) into snake case, case-insensitive identifiers.

Each destination declares its preferred naming convention, support for case-sensitive identifiers, and case folding function that case-insensitive identifiers follow. For example:

1. Redshift - by default, does not support case-sensitive identifiers and converts all of them to lower case.
2. Snowflake - supports case-sensitive identifiers and considers upper-cased identifiers as case-insensitive (which is the default case folding).
3. DuckDb - does not support case-sensitive identifiers but does not case fold them, so it preserves the original casing in the information schema.
4. Athena - does not support case-sensitive identifiers and converts all of them to lower case.
5. BigQuery - all identifiers are case-sensitive; there's no case-insensitive mode available via case folding (but it can be enabled at the dataset level).

You can change the naming convention used in many different ways. Below, we set the preferred naming convention on the Snowflake destination to sql_cs to switch Snowflake to case-sensitive mode:

import dlt
snow_ = dlt.destinations.snowflake(naming_convention="sql_cs_v1")

Setting the naming convention will impact all new schemas being created (i.e., on the first pipeline run) and will re-normalize all existing identifiers.

warning

dlt prevents re-normalization of identifiers in tables that were already created at the destination. Use refresh mode to drop the data. You can also disable this behavior via configuration.

note

Destinations that support case-sensitive identifiers but use a case folding convention to enable case-insensitive identifiers are configured in case-insensitive mode by default. Examples: Postgres, Snowflake, Oracle.

warning

If you use a case-sensitive naming convention with a case-insensitive destination, dlt will:

1. Fail the load if it detects an identifier collision due to case folding.
2. Warn if any case folding is applied by the destination.

Enable case-sensitive identifiers support

Selected destinations may be configured so they start accepting case-sensitive identifiers. For example, it is possible to set case-sensitive collation on an mssql database and then tell dlt about it.

from dlt.destinations import mssql
dest_ = mssql(has_case_sensitive_identifiers=True, naming_convention="sql_cs_v1")

Above, we can safely use a case-sensitive naming convention without worrying about name collisions.

You can configure the case sensitivity, but configuring destination capabilities is not currently supported.

[destination.mssql]
has_case_sensitive_identifiers=true

note

In most cases, setting the flag above just indicates to dlt that you switched the case-sensitive option on a destination. dlt will not do that for you. Refer to the destination documentation for details.

Create a new destination

You have two ways to implement a new destination:

1. You can use the @dlt.destination decorator and implement a sink function. This is a perfect way to implement reverse ETL destinations that push data back to REST APIs.
2. You can implement a full destination where you have full control over load jobs and schema migration.