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AWS Athena / Glue Catalog

The athena destination stores data as parquet files in s3 buckets and creates external tables in aws athena. You can then query those tables with athena sql commands which will then scan the whole folder of parquet files and return the results. This destination works very similar to other sql based destinations, with the exception of the merge write disposition not being supported at this time. dlt metadata will be stored in the same bucket as the parquet files, but as iceberg tables. Athena additionally supports writing individual data tables as iceberg tables, so the may be manipulated later, a common use-case would be to strip gdpr data from them.

Setup Guide

1. Initialize the dlt project

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

dlt init chess athena

💡 This command will initialise your pipeline with chess as the source and aws athena as the destination using the filesystem staging destination

2. Setup bucket storage and athena credentials

First install dependencies by running:

pip install -r requirements.txt

or with pip install dlt[athena] which will install s3fs, pyarrow, pyathena and botocore packages.


You may also install the dependencies independently try

pip install dlt
pip install s3fs
pip install pyarrow
pip install pyathena

so pip does not fail on backtracking

To edit the dlt credentials file with your secret info, open .dlt/secrets.toml. You will need to provide a bucket_url which holds the uploaded parquet files, a query_result_bucket which athena uses to write query results too, and credentials that have write and read access to these two buckets as well as the full athena access aws role.

The toml file 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

query_result_bucket="s3://[results_bucket_name]" # replace with your query results bucket name

aws_access_key_id="please set me up!" # same as credentials for filesystem
aws_secret_access_key="please set me up!" # same as credentials for filesystem
region_name="please set me up!" # set your aws region, for example "eu-central-1" for frankfurt

if you have your credentials stored in ~/.aws/credentials just remove the [destination.filesystem.credentials] and [destination.athena.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):



Additional Destination Configuration

You can provide an athena workgroup like so:


Write disposition

athena destination handles the write dispositions as follows:

  • append - files belonging to such tables are added to dataset folder
  • replace - all files that belong to such tables are deleted from dataset folder and then current set of files is added.
  • merge - falls back to append

Data loading

Data loading happens by storing parquet files in an s3 bucket and defining a schema on athena. If you query data via sql queries on athena, the returned data is read by scanning your bucket and reading all relevant parquet files in there.

dlt internal tables are saved as Iceberg tables.

Data types

Athena tables store timestamps with millisecond precision and with that precision we generate parquet files. Mind that Iceberg tables have microsecond precision.

Athena does not support JSON fields so JSON is stored as string.

Athena does not support TIME columns in parquet files. dlt will fail such jobs permanently. Convert datetime.time objects to str or datetime.datetime to load them.

Naming Convention

We follow our snake_case name convention. Mind the following:

  • DDL use HIVE escaping with ``````
  • Other queries use PRESTO and regular SQL escaping.

Staging support

Using a staging destination is mandatory when using the athena destination. If you do not set staging to filesystem, dlt will automatically do this for you.

If you decide to change the filename layout from the default value, keep the following in mind so that Athena can reliably build your tables:

  • You need to provide the {table_name} placeholder and this placeholder needs to be followed by a forward slash
  • You need to provide the {file_id} placeholder and it needs to be somewhere after the {table_name} placeholder.
  • {table_name} must be the first placeholder in the layout.

Additional destination options

iceberg data tables

You can save your tables as iceberg tables to athena. This will enable you to for example delete data from them later if you need to. To switch a resouce to the iceberg table-format, supply the table_format argument like this:

def data() -> Iterable[TDataItem]:

Alternatively you can set all tables to use the iceberg format with a config variable:

force_iceberg = "True"

For every table created as an iceberg table, the athena destination will create a regular athena table in the staging dataset of both the filesystem as well as the athena glue catalog and then copy all data into the final iceberg table that lives with the non-iceberg tables in the same dataset on both filesystem and the glue catalog. Switching from iceberg to regular table or vice versa is not supported.

dbt support

Athena is supported via dbt-athena-community. Credentials are passed into aws_access_key_id and aws_secret_access_key of generated dbt profile. Iceberg tables are supported but you need to make sure that you materialize your models as iceberg tables if your source table is iceberg. We encountered problems with materializing date time columns due to different precision on iceberg (nanosecond) and regular Athena tables (millisecond). The Athena adapter requires that you setup region_name in Athena configuration below. You can also setup table catalog name to change the default: awsdatacatalog


Syncing of dlt state

  • This destination fully supports dlt state sync.. The state is saved in athena iceberg tables in your s3 bucket.

Supported file formats

You can choose the following file formats:

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!


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