Skip to main content

Filesystem & buckets

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

💡 Please read the notes on the layout of the data files. Currently, we are getting feedback on it. Please join our Slack (icon at the top of the page) and help us find the optimal layout.

Install dlt with filesystem

To install the dlt library with filesystem dependencies:

pip install "dlt[filesystem]"

This installs s3fs and botocore packages.


You may also install the dependencies independently. Try:

pip install dlt
pip install s3fs

so pip does not fail on backtracking.

Initialise the dlt project

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

dlt init chess filesystem

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

Set up bucket storage and credentials


The command above creates a sample secrets.toml and requirements file for 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:

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

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):


You can also pass an AWS region:


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:

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

  4. Below you can find a sample policy that gives a 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": [
"Resource": [
  1. To grab 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.
  2. Grab 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 like MinIO or Cloudflare R2, you may supply an endpoint_url in the config. This should be set along with AWS credentials:

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

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>" # copy your endpoint URL here

Adding Additional Configuration

To pass any additional arguments to fsspec, you may supply kwargs and client_kwargs in the config as a stringified dictionary:

kwargs = '{"use_ssl": true, "auto_mkdir": true}'
client_kwargs = '{"verify": "public.crt"}'

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

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

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 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.

Two forms of Azure credentials are supported:

SAS token credentials

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

bucket_url = "az://[your_container name]" # replace with your container name

# 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

bucket_url = "az://[your_container name]" # replace with your container name

azure_client_id = "client_id" # please set me up!
azure_client_secret = "client_secret"
azure_tenant_id = "tenant_id" # please set me up!

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)

bucket_url = "file:///absolute/path" # three / for an absolute path

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:

kwargs = '{"auto_mkdir": true}'

Or by setting environment variable:

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

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

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.

bucket_url = '\\localhost\c$\a\b\c' # UNC equivalent of C:\a\b\c

bucket_url = '/var/local/data' # absolute POSIX style path

bucket_url = '_storage/data' # relative POSIX style path

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

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

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



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

To go over this limit you can use extended paths. dlt recognizes both regular and UNC extended paths

bucket_url = '\\?\C:\a\b\c'


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, which may result in the files being stored in a compressed format. This format may not be easily readable as plain text or JSON Lines (jsonl) files. If you encounter files that seem unreadable, they may be compressed.

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:
  • 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.

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.


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

You can control 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}


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 - table name
  • load_id - the id of the load package from which the file comes from
  • file_id - the id of the file, is there are many files with data for a single table, they are copied with different file ids
  • ext - a format of the file i.e. jsonl or parquet

Date and time placeholders


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

Both timestamp_ms and load_package_timestamp_ms are in milliseconds (e.g., 12334455233), not fractional seconds to make sure 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:

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={year}/month={month}/day={day}/{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 the 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 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:

layout = "{table_name}/{test_placeholder}/{YYYY}-{MM}-{DD}/{ddd}/{mm}/{load_id}.{file_id}.{ext}"
extra_placeholders = { "test_placeholder" = "test_value" }
# for automatic directory creation in the local filesystem
kwargs = '{"auto_mkdir": true}'

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(
"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 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:

pipeline = dlt.pipeline(
"placeholder_x": placeholder_callback

The currently recommended layout structure is straightforward:


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:

Supported table formats

You can choose the following table formats:

Delta table format

You need the deltalake package to use this format:

pip install "dlt[deltalake]"

Set the table_format argument to delta when defining your resource:

def my_delta_resource():

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

Storage options

You can pass storage options by configuring destination.filesystem.deltalake_storage_options:

deltalake_storage_options = '{"AWS_S3_LOCKING_PROVIDER": "dynamodb", DELTA_DYNAMO_TABLE_NAME": "custom_table_name"}'

dlt passes these options to the storage_options argument of the write_deltalake method in the deltalake library. 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.

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

Syncing of dlt state

This destination fully supports dlt state sync. To this end, special folders and files that 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.

Additional Setup guides

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!


Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.