Cloud storage and filesystem

The filesystem destination stores data in remote file systems and cloud storage services like AWS S3, Google Cloud Storage, or Azure Blob Storage. Underneath, it uses fsspec to abstract file operations. Its primary role is to be used as a staging area for other destinations, but you can also quickly build a data lake with it.

Install dlt with filesystem

Install the dlt library with filesystem dependencies:

pip install "dlt[filesystem]"

This installs the s3fs and botocore packages.

warning

You may also install the dependencies independently. Try:

pip install dlt
pip install s3fs

so pip does not fail on backtracking.

Destination capabilities

The following table shows the capabilities of the Filesystem destination:

Feature	Value	More
Preferred loader file format	jsonl	File formats
Supported loader file formats	jsonl, insert_values, parquet, csv, model, reference	File formats
Supported table formats	delta, iceberg	Table formats
Has case sensitive identifiers	True	Naming convention
Supported merge strategies	upsert, insert-only	Merge strategy
Supported replace strategies	truncate-and-insert, insert-from-staging	Replace strategy
Sqlglot dialect	duckdb	Dataset access
Supports tz aware datetime	True	Timestamps and Timezones
Supports naive datetime	True	Timestamps and Timezones

This table shows the supported features of the Filesystem destination in dlt.

Initialize the dlt project

Let's start by initializing a new dlt project as follows:

dlt init chess filesystem

note

This command will initialize your pipeline with chess as the source and AWS S3 as the destination.

Set up the destination and credentials

AWS S3

The command above creates a sample secrets.toml and requirements file for an AWS S3 bucket. You can install those dependencies by running:

pip install -r requirements.txt

To edit the dlt credentials file with your secret info, open .dlt/secrets.toml, which looks like this:

[destination.filesystem]
bucket_url = "s3://[your_bucket_name]" # replace with your bucket name,

[destination.filesystem.credentials]
aws_access_key_id = "please set me up!" # copy the access key here
aws_secret_access_key = "please set me up!" # copy the secret access key here

If you have your credentials stored in ~/.aws/credentials, just remove the [destination.filesystem.credentials] section above, and dlt will fall back to your default profile in local credentials. If you want to switch the profile, pass the profile name as follows (here: dlt-ci-user):

[destination.filesystem.credentials]
profile_name="dlt-ci-user"

You can also specify an AWS region:

[destination.filesystem.credentials]
region_name="eu-central-1"

You need to create an S3 bucket and a user who can access that bucket. dlt does not create buckets automatically.

1. You can create the S3 bucket in the AWS console by clicking on "Create Bucket" in S3 and assigning the appropriate name and permissions to the bucket.

2. Once the bucket is created, you'll have the bucket URL. For example, if the bucket name is dlt-ci-test-bucket, then the bucket URL will be:

   s3://dlt-ci-test-bucket

3. To grant permissions to the user being used to access the S3 bucket, go to IAM > Users, and click on “Add Permissions”.

4. Below you can find a sample policy that gives the minimum permission required by dlt to a bucket we created above. The policy contains permissions to list files in a bucket, get, put, and delete objects. Remember to place your bucket name in the Resource section of the policy!

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "DltBucketAccess",
            "Effect": "Allow",
            "Action": [
                "s3:PutObject",
                "s3:GetObject",
                "s3:DeleteObject",
                "s3:GetObjectAttributes",
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::dlt-ci-test-bucket/*",
                "arn:aws:s3:::dlt-ci-test-bucket"
            ]
        }
    ]
}

5. To obtain the access and secret key for the user, go to IAM > Users and in the “Security Credentials”, click on “Create Access Key”, and preferably select “Command Line Interface” and create the access key.
6. Obtain the “Access Key” and “Secret Access Key” created that are to be used in "secrets.toml".

Using S3 compatible storage

To use an S3 compatible storage other than AWS S3, such as MinIO, Cloudflare R2 or Google Cloud Storage, you may supply an endpoint_url in the config. This should be set along with AWS credentials:

[destination.filesystem]
bucket_url = "s3://[your_bucket_name]" # replace with your bucket name,

[destination.filesystem.credentials]
aws_access_key_id = "please set me up!" # copy the access key here
aws_secret_access_key = "please set me up!" # copy the secret access key here
endpoint_url = "https://<account_id>.r2.cloudflarestorage.com" # copy your endpoint URL here

Adding additional configuration

To pass any additional arguments to fsspec, you may supply kwargs and client_kwargs in toml config.

[destination.filesystem.kwargs]
use_ssl=true
auto_mkdir=true

[destination.filesystem.client_kwargs]
verify="public.crt"

To pass additional arguments via env variables, use stringified dictionary: DESTINATION__FILESYSTEM__KWARGS='{"use_ssl": true, "auto_mkdir": true}

You can also override default fsspec settings used by dlt:

[destination.filesystem.kwargs]
use_listings_cache=false  # listing cache disabled by default as you typically add files
listings_expiry_time=60.0
skip_instance_cache=false  # instance cache enabled by default, it is thread isolated anyway

There's however no good reason to do that, except debugging fsspec internal problems. You could try to enable listing cache but this cache is not shared across threads which dlt load steps uses to parallelize writes. You may get unpredictable cache invalidation behavior.

Google storage

Run pip install "dlt[gs]" which will install the gcfs package.

To edit the dlt credentials file with your secret info, open .dlt/secrets.toml. You'll see AWS credentials by default. Use Google cloud credentials that you may know from BigQuery destination

[destination.filesystem]
bucket_url = "gs://[your_bucket_name]" # replace with your bucket name,

[destination.filesystem.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # please set me up!

note

Note that you can share the same credentials with BigQuery, replace the [destination.filesystem.credentials] section with a less specific one: [destination.credentials] which applies to both destinations.

If you have default Google Cloud credentials in your environment (i.e., on cloud function), remove the credentials sections above and dlt will fall back to the available default.

Use Cloud Storage admin to create a new bucket. Then assign the Storage Object Admin role to your service account.

Azure Blob Storage

Run pip install "dlt[az]" which will install the adlfs package to interface with Azure Blob Storage.

Edit the credentials in .dlt/secrets.toml, you'll see AWS credentials by default; replace them with your Azure credentials.

Supported schemes

dlt supports both forms of the blob storage urls:

[destination.filesystem]
bucket_url = "az://<container_name>/path" # replace with your container name and path

and

[destination.filesystem]
bucket_url = "abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/path"

You can use az, abfss, azure and abfs url schemes.

If you need to use a custom host for your storage account, you can set it up like below:

[destination.filesystem.credentials]
# The storage account name is always required
azure_account_host = "<storage_account_name>.<host_base>"

Remember to include storage_account_name with your base host i.e. dlt_ci.blob.core.usgovcloudapi.net.

dlt will use this host to connect to Azure Blob Storage without any modifications:

OneLake (Fabric)

Use the Blob endpoint (azure_account_host = "onelake.blob.fabric.microsoft.com").

IMPORTANT: OneLake bucket URLs must use GUIDs for workspace and lakehouse, not display names:

bucket_url = "abfss://<workspace_guid>@onelake.dfs.fabric.microsoft.com/<lakehouse_guid>/Files"

Find GUIDs in your browser URL when viewing workspace/lakehouse in Fabric portal.

Two forms of Azure credentials are supported:

SAS token credentials

Supply storage account name and either SAS token or storage account key

[destination.filesystem.credentials]
# The storage account name is always required
azure_storage_account_name = "account_name" # please set me up!
# You can set either account_key or sas_token, only one is needed
azure_storage_account_key = "account_key" # please set me up!
azure_storage_sas_token = "sas_token" # please set me up!

If you have the correct Azure credentials set up on your machine (e.g., via Azure CLI), you can omit both azure_storage_account_key and azure_storage_sas_token and dlt will fall back to the available default. Note that azure_storage_account_name is still required as it can't be inferred from the environment.

Service principal credentials

Supply a client ID, client secret, and a tenant ID for a service principal authorized to access your container.

[destination.filesystem.credentials]
azure_storage_account_name = "account_name" # please set me up!
azure_client_id = "client_id" # please set me up!
azure_client_secret = "client_secret"
azure_tenant_id = "tenant_id" # please set me up!

warning

Concurrent blob uploads dlt limits the number of concurrent connections for a single uploaded blob to 1. By default, adlfs that we use splits blobs into 4 MB chunks and uploads them concurrently, which leads to gigabytes of used memory and thousands of connections for larger load packages. You can increase the maximum concurrency as follows:

[destination.filesystem.kwargs]
max_concurrency=3

Hugging Face

The filesystem destination supports loading into Hugging Face Datasets using the hf:// protocol. See the Hugging Face destination page for setup and configuration details.

Local file system

If for any reason you want to have those files in a local folder, set up the bucket_url as follows (you are free to use config.toml for that as there are no secrets required):

[destination.filesystem]
bucket_url = "file:///absolute/path"  # three slashes (file:///) for an absolute path

tip

For handling deeply nested layouts, consider enabling automatic directory creation for the local filesystem destination. This can be done by setting kwargs in secrets.toml:

[destination.filesystem]
kwargs = '{"auto_mkdir": true}'

Or by setting an environment variable:

export DESTINATION__FILESYSTEM__KWARGS = '{"auto_mkdir": true/false}'

dlt correctly handles the native local file paths. Indeed, using the file:// schema may not be intuitive, especially for Windows users.

[destination.unc_destination]
bucket_url = 'C:\a\b\c'

In the example above, we specify bucket_url using TOML's literal strings that do not require escaping of backslashes.

[destination.unc_destination]
bucket_url = '\\localhost\c$\a\b\c'  # UNC equivalent of C:\a\b\c

[destination.posix_destination]
bucket_url = '/var/local/data'  # absolute POSIX style path

[destination.relative_destination]
bucket_url = '_storage/data'  # relative POSIX style path

In the examples above, we define a few named filesystem destinations:

• unc_destination demonstrates a Windows UNC path in native form.
• posix_destination demonstrates a native POSIX (Linux/Mac) absolute path.
• relative_destination demonstrates a native POSIX (Linux/Mac) relative path. In this case, the filesystem destination will store files in the $cwd/_storage/data path, where $cwd is your current working directory.

dlt supports Windows UNC paths with the file:// scheme. They can be specified using host or purely as a path component.

[destination.unc_with_host]
bucket_url="file://localhost/c$/a/b/c"

[destination.unc_with_path]
bucket_url="file:////localhost/c$/a/b/c"

warning

Windows supports paths up to 255 characters. When you access a path longer than 255 characters, you'll see a FileNotFound exception.

To overcome this limit, you can use extended paths. dlt recognizes both regular and UNC extended paths.

[destination.regular_extended]
bucket_url = '\\?\C:\a\b\c'

[destination.unc_extended]
bucket_url='\\?\UNC\localhost\c$\a\b\c'

SFTP

Run pip install "dlt[sftp]" which will install the paramiko package alongside dlt, enabling secure SFTP transfers.

Configure your SFTP credentials by editing the .dlt/secrets.toml file. By default, the file contains placeholders for AWS credentials. You should replace these with your SFTP credentials.

Below are the possible fields for SFTP credentials configuration:

sftp_port                   # The port for SFTP, defaults to 22 (standard for SSH/SFTP)
sftp_username               # Your SFTP username, defaults to None
sftp_password               # Your SFTP password (if using password-based auth), defaults to None
*sftp_pkey*                 # Your private key for key-based authentication, defaults to None
sftp_key_filename           # Path to your private key file for key-based authentication, defaults to None
sftp_key_passphrase         # Passphrase for your private key (if applicable), defaults to None
sftp_timeout                # Timeout for establishing a connection, defaults to None
sftp_banner_timeout         # Timeout for receiving the banner during authentication, defaults to None
sftp_auth_timeout           # Authentication timeout, defaults to None
sftp_channel_timeout        # Channel timeout for SFTP operations, defaults to None
sftp_allow_agent            # Use SSH agent for key management (if available), defaults to True
sftp_look_for_keys          # Search for SSH keys in the default SSH directory (~/.ssh/), defaults to True
sftp_compress               # Enable compression (can improve performance over slow networks), defaults to False
*sftp_sock*                 # Custom socket to use for communication to target host, defaults to None
sftp_gss_auth               # Use GSS-API for authentication, defaults to False
sftp_gss_kex                # Use GSS-API for key exchange, defaults to False
sftp_gss_deleg_creds        # Delegate credentials with GSS-API, defaults to True
sftp_gss_host               # Host for GSS-API, defaults to None
sftp_gss_trust_dns          # Trust DNS for GSS-API, defaults to True
*sftp_disabled_algorithms*  # Disable specific algorithms for security, defaults to None
*sftp_transport_factory*    # Custom transport factory, defaults to None
*sftp_auth_strategy*        # Authentication strategy, defaults to None

note

The * credentials indicate parameters that cannot be set through .dlt/secrets.toml and must be set through code instantiation.

info

For more information about credentials parameters: https://docs.paramiko.org/en/3.3/api/client.html#paramiko.client.SSHClient.connect

Authentication methods

SFTP authentication is attempted in the following order of priority:

1. Key-based authentication: If you provide a key_filename containing the path to a private key or a corresponding OpenSSH public certificate (e.g., id_rsa and id_rsa-cert.pub), these will be used for authentication. If the private key requires a passphrase, you can specify it via sftp_key_passphrase. If your private key requires a passphrase to unlock, and you’ve provided one, it will be used to attempt to unlock the key.

2. SSH Agent-based authentication: If allow_agent=True (default), Paramiko will look for any SSH keys stored in your local SSH agent (such as id_rsa, id_dsa, or id_ecdsa keys stored in ~/.ssh/).

3. Username/Password authentication: If a password is provided (sftp_password), plain username/password authentication will be attempted.

4. GSS-API authentication: If GSS-API (Kerberos) is enabled (sftp_gss_auth=True), authentication will use the Kerberos protocol. GSS-API may also be used for key exchange (sftp_gss_kex=True) and credential delegation (sftp_gss_deleg_creds=True). This method is useful in environments where Kerberos is set up, often in enterprise networks.

1. Key-based authentication

If you use an SSH key instead of a password, you can specify the path to your private key in the configuration.

[destination.filesystem]
bucket_url = "sftp://[hostname]/[path]"
file_glob = "*"

[destination.filesystem.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

2. SSH agent-based authentication

If you have an SSH agent running with loaded keys, you can allow Paramiko to use these keys automatically. You can omit the password and key fields if you're relying on the SSH agent.

[destination.filesystem]
bucket_url = "sftp://[hostname]/[path]"
file_glob = "*"

[destination.filesystem.credentials]
sftp_username = "foo"
sftp_key_passphrase = "your_passphrase"   # Optional: passphrase for your private key

The loaded key must be one of the following types stored in ~/.ssh/: id_rsa, id_dsa, or id_ecdsa.

3. Username and password authentication

This is the simplest form of authentication, where you supply a username and password directly.

[destination.filesystem]
bucket_url = "sftp://[hostname]/[path]"  # The hostname of your SFTP server and the remote path
file_glob = "*"                          # Pattern to match the files you want to upload/download

[destination.filesystem.credentials]
sftp_username = "foo"                    # Replace "foo" with your SFTP username
sftp_password = "pass"                   # Replace "pass" with your SFTP password

Notes:

• Key-based authentication: Make sure your private key has the correct permissions (chmod 600), or SSH will refuse to use it.
• Timeouts: It's important to adjust timeout values based on your network conditions to avoid connection issues.

This configuration allows flexible SFTP authentication, whether you're using passwords, keys, or agents, and ensures secure communication between your local environment and the SFTP server.

Write disposition

The filesystem 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 - falls back to append

File compression

The filesystem destination in the dlt library uses gzip compression by default for efficiency.

To handle compressed files:

• To disable compression, you can modify the data_writer.disable_compression setting in your "config.toml" file. This can be useful if you want to access the files directly without needing to decompress them. For example:

[normalize.data_writer]
disable_compression=true

• To decompress a gzip file, you can use tools like gunzip. This will convert the compressed file back to its original format, making it readable.

note

Starting with dlt version 1.15.0, compressed csv and jsonl files automatically include a .gz extension to reflect their gzip-compressed format. In versions prior to 1.15.0, compressed files were saved without the .gz extension. If you have a dataset created with an earlier version (e.g., 1.14.0 or below), dlt will automatically detect the older format and preserve the original naming (without .gz) for that dataset. New datasets created with 1.15.0 or later will include the .gz extension by default.

For more details on managing file compression, please visit our documentation on performance optimization: Disabling and enabling file compression.

Files layout

All the files are stored in a single folder with the name of the dataset that you passed to the run or load methods of the pipeline. In our example chess pipeline, it is chess_players_games_data.

note

Object storages are, in fact, key-blob storage, so the folder structure is emulated by splitting file names into components by a separator (/).

You can control the files layout by specifying the desired configuration. There are several ways to do this.

Default layout

Current default layout: {table_name}/{load_id}.{file_id}.{ext}

note

The default layout format has changed from {schema_name}.{table_name}.{load_id}.{file_id}.{ext} to {table_name}/{load_id}.{file_id}.{ext} in dlt 0.3.12. You can revert to the old layout by setting it manually.

Available layout placeholders

Standard placeholders

• schema_name - the name of the schema
• table_name - the table name
• load_id - the ID of the load package from which the file comes
• file_id - the ID of the file; if there are many files with data for a single table, they are copied with different file IDs
• ext - the format of the file, i.e., jsonl or parquet

Date and time placeholders

tip

Keep in mind all values are lowercased.

• timestamp - the current timestamp in Unix Timestamp format rounded to seconds
• timestamp_ms - the current timestamp in Unix Timestamp format in milliseconds
• load_package_timestamp - timestamp from load package in Unix Timestamp format rounded to seconds
• load_package_timestamp_ms - timestamp from load package in Unix Timestamp format in milliseconds

note

Both timestamp_ms and load_package_timestamp_ms are in milliseconds (e.g., 12334455233), not fractional seconds to ensure millisecond precision without decimals.

• Years
  • YYYY - 2024, 2025
  • Y - 2024, 2025
• Months
  • MMMM - January, February, March
  • MMM - Jan, Feb, Mar
  • MM - 01, 02, 03
  • M - 1, 2, 3
• Days of the month
  • DD - 01, 02
  • D - 1, 2
• Hours 24h format
  • HH - 00, 01, 02...23
  • H - 0, 1, 2...23
• Minutes
  • mm - 00, 01, 02...59
  • m - 0, 1, 2...59
• Seconds
  • ss - 00, 01, 02...59
  • s - 0, 1, 2...59
• Fractional seconds
  • SSSS - 000[0..] 001[0..] ... 998[0..] 999[0..]
  • SSS - 000 001 ... 998 999
  • SS - 00, 01, 02 ... 98, 99
  • S - 0 1 ... 8 9
• Days of the week
  • dddd - Monday, Tuesday, Wednesday
  • ddd - Mon, Tue, Wed
  • dd - Mo, Tu, We
  • d - 0-6
• Q - quarters 1, 2, 3, 4

You can change the file name format by providing the layout setting for the filesystem destination like so:

[destination.filesystem]
layout="{table_name}/{load_id}.{file_id}.{ext}" # current preconfigured naming scheme

# More examples
# With timestamp
# layout = "{table_name}/{timestamp}/{load_id}.{file_id}.{ext}"

# With timestamp of the load package
# layout = "{table_name}/{load_package_timestamp}/{load_id}.{file_id}.{ext}"

# Parquet-like layout (note: it is not compatible with the internal datetime of the parquet file)
# layout = "{table_name}/year={YYYY}/month={MM}/day={DD}/{load_id}.{file_id}.{ext}"

# Custom placeholders
# extra_placeholders = { "owner" = "admin", "department" = "finance" }
# layout = "{table_name}/{owner}/{department}/{load_id}.{file_id}.{ext}"

A few things to know when specifying your filename layout:

• If you want a different base path that is common to all filenames, you can suffix your bucket_url rather than prefix your layout setting.
• If you do not provide the {ext} placeholder, it will automatically be added to your layout at the end with a dot as a separator.
• It is best practice to have a separator between each placeholder. Separators can be any character allowed as a filename character, but dots, dashes, and forward slashes are most common.
• When you are using the replace disposition, dlt will have to be able to figure out the correct files to delete before loading the new data. For this to work, you have to:
  • include the {table_name} placeholder in your layout
  • not have any other placeholders except for the {schema_name} placeholder before the table_name placeholder and
  • have a separator after the table_name placeholder

Please note:

• dlt will mark complete loads by creating a json file in the ./_dlt_loads folders that corresponds to the _dlt_loads table. For example, if the chess__1685299832.jsonl file is present in the loads folder, you can be sure that all files for the load package 1685299832 are completely loaded.

Advanced layout configuration

The filesystem destination configuration supports advanced layout customization and the inclusion of additional placeholders. This can be done through config.toml or programmatically when initializing via a factory method.

Configuration via config.toml

To configure the layout and placeholders using config.toml, use the following format:

[destination.filesystem]
layout = "{table_name}/{test_placeholder}/{YYYY}-{MM}-{DD}/{ddd}/{mm}/{load_id}.{file_id}.{ext}"
extra_placeholders = { "test_placeholder" = "test_value" }
current_datetime="2024-04-14T00:00:00"
# for automatic directory creation in the local filesystem
kwargs = '{"auto_mkdir": true}'

note

Ensure that the placeholder names match the intended usage. For example, {test_placeholer} should be corrected to {test_placeholder} for consistency.

Dynamic configuration in the code

Configuration options, including layout and placeholders, can be overridden dynamically when initializing and passing the filesystem destination directly to the pipeline.

import pendulum

import dlt
from dlt.destinations import filesystem

pipeline = dlt.pipeline(
    pipeline_name="data_things",
    destination=filesystem(
        layout="{table_name}/{test_placeholder}/{timestamp}/{load_id}.{file_id}.{ext}",
        current_datetime=pendulum.now(),
        extra_placeholders={
            "test_placeholder": "test_value",
        }
    )
)

Furthermore, it is possible to:

1. Customize the behavior with callbacks for extra placeholder functionality. Each callback must accept the following positional arguments and return a string.
2. Customize the current_datetime, which can also be a callback function and is expected to return a pendulum.DateTime instance.

import pendulum

import dlt
from dlt.destinations import filesystem

def placeholder_callback(schema_name: str, table_name: str, load_id: str, file_id: str, ext: str) -> str:
    # Custom logic here
    return "custom_value"

def get_current_datetime() -> pendulum.DateTime:
    return pendulum.now()

pipeline = dlt.pipeline(
    pipeline_name="data_things",
    destination=filesystem(
        layout="{table_name}/{placeholder_x}/{timestamp}/{load_id}.{file_id}.{ext}",
        current_datetime=get_current_datetime,
        extra_placeholders={
            "placeholder_x": placeholder_callback
        }
    )
)

Recommended layout

The currently recommended layout structure is straightforward:

layout="{table_name}/{load_id}.{file_id}.{ext}"

Adopting this layout offers several advantages:

1. Efficiency: It's fast and simple to process.
2. Compatibility: Supports replace as the write disposition method.
3. Flexibility: Compatible with various destinations, including Athena.
4. Performance: A deeply nested structure can slow down file navigation, whereas a simpler layout mitigates this issue.

Supported file formats

You can choose the following file formats:

• JSONL is used by default
• Parquet is supported
• CSV is supported

Supported table formats

You can choose the following table formats:

• Delta table
• Iceberg

Syncing of dlt state

This destination fully supports dlt state sync. To this end, special folders and files will be created at your destination which hold information about your pipeline state, schemas, and completed loads. These folders DO NOT respect your settings in the layout section. When using filesystem as a staging destination, not all of these folders are created, as the state and schemas are managed in the regular way by the final destination you have configured.

You will also notice init files being present in the root folder and the special dlt folders. In the absence of the concepts of schemas and tables in blob storages and directories, dlt uses these special files to harmonize the behavior of the filesystem destination with the other implemented destinations.

note

When a load generates a new state, for example when using incremental loads, a new state file appears in the _dlt_pipeline_state folder at the destination. To prevent data accumulation, state cleanup mechanisms automatically remove old state files, retaining only the latest 100 by default. This cleanup process can be customized or disabled using the filesystem configuration max_state_files, which determines the maximum number of pipeline state files to retain (default is 100). Setting this value to 0 or a negative number disables the cleanup of old states.

Data access

filesystem implements sql_client which provides read only SQL access to files and iceberg/delta tables with duckdb dialect. This also enables pipeline.dataset(), giving you Python-native access to loaded data as Pandas DataFrames, PyArrow tables, or Python tuples.

By default views that are created are "frozen" to minimize reading from bucket. You can enable views autorefresh:

[destination.filesystem]
always_refresh_views=true

Troubleshooting

File Name Too Long Error

When running your pipeline, you might encounter an error like [Errno 36] File name too long Error. This error occurs because the generated file name exceeds the maximum allowed length on your filesystem.

To prevent the file name length error, set the max_identifier_length parameter for your destination. This truncates all identifiers (including filenames) to a specified maximum length. For example:

from dlt.destinations import duckdb as duckdb_destination

pipeline = dlt.pipeline(
    pipeline_name="your_pipeline_name",
    destination=duckdb_destination(
        max_identifier_length=200,  # Adjust the length as needed
    ),
)

note

• max_identifier_length truncates all identifiers (tables, columns). Ensure the length maintains uniqueness to avoid collisions.
• Adjust max_identifier_length based on your data structure and filesystem limits.

Additional Setup guides

• Load data from DigitalOcean to AWS S3 in python with dlt
• Load data from Fivetran to Google Cloud Storage in python with dlt
• Load data from Pinterest to The Local Filesystem in python with dlt
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