AWS Athena#

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 scan the entire folder of Parquet files and return the results. This destination works very similarly to other SQL-based destinations, with the exception that the merge write disposition is not supported at this time. The dlt metadata will be stored in the same bucket as the Parquet files, but as iceberg tables. Athena also supports writing individual data tables as Iceberg tables, so they may be manipulated later. A common use case would be to strip GDPR data from them.

Install dlt with Athena#

To install the dlt library with Athena dependencies:

pip install "dlt[athena]"

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 initialize 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.

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, and credentials that have write and read access to the bucket as well as the full Athena access AWS role. You should also provide a query_result_bucket, which Athena uses to write query results to. If your Athena workgroup has Managed Query Results enabled, omit query_result_bucket (or leave it unset) — Athena will manage the results storage automatically.

The TOML file 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

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

[destination.athena.credentials]
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] sections 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"

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

Additional destination configuration#

You can provide an Athena workgroup like so:

[destination.athena]
athena_work_group="my_workgroup"

You can force all tables to be in iceberg format and control Iceberg tables layout in the bucket:

[destination.athena]
force_iceberg=true
table_location_layout="{dataset_name}/{table_name}"

where dataset_name and table_name will be replaced by actual names.

You can change the default catalog name

[destination.athena]
aws_data_catalog="awsdatacatalog"

By default, dlt uses the same catalog for both the staging and production (non-staging) tables. You can explicitly set the staging catalog to use separate catalogs:

[destination.athena]
staging_aws_data_catalog="my-staging-catalog"

and provide any other PyAthena connection setting

[destination.athena.conn_properties]
poll_interval=2

When dlt executes SQL queries that look up table names in the INFORMATION_SCHEMA
it can be more effective to filter the tables in the code instead of doing it in the query.
The threshold for this is usually 1000 tables, but we learned that for Athena a better default is
90. You can change this threshold with the following setting:

[destination.athena]
info_tables_query_threshold=90

You can specify the database location using db_location:

[destination.athena]
db_location="s3://[your_bucket_name]" # replace with your bucket name

S3 Tables#

The athena destination supports storing data in an S3 Tables bucket by setting an S3 Tables Catalog:

[destination.athena]
aws_data_catalog="s3tablescatalog/[your_table_bucket_name]" # replace with your table bucket name

dlt assumes you already have a table bucket that's integrated with AWS analytics services. If that's not the case, look at these instructions.

Using S3 Tables with Athena implies that:

• data is stored in Iceberg tables
• table locations are managed by the S3 Tables Catalog
• the table_location_layout setting is ignored
• the s3_tables naming convention is used
• production (non-staging) and staging tables are registered in separate catalogs:
  • production: S3 Tables Catalog
  • staging: regular catalog

When staging_aws_data_catalog is not specified, dlt normally uses the same catalog for both staging and production tables. However, when using an S3 Tables Catalog, the staging catalog defaults to awsdatacatalog because staging tables are not Iceberg tables and cannot be registered in the S3 Tables Catalog.

Write disposition#

The athena 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 (unless you're using iceberg tables).

Data loading#

Data loading occurs 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. Keep in mind that Iceberg tables have microsecond precision.

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

Table and column identifiers#

Athena uses case-insensitive identifiers and will lowercase all the identifiers that are stored in the INFORMATION SCHEMA. Do not use case-sensitive naming conventions. Letter casing will be removed anyway, and you risk generating identifier collisions, which are detected by dlt and will fail the load process.

Under the hood, Athena uses different SQL engines for DDL (catalog) and DML/Queries:

• DDL uses 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, for example, to delete data from them later if you need to. To switch a resource to the Iceberg table format, supply the table_format argument like this:

@dlt.resource(table_format="iceberg")
def data() -> Iterable[TDataItem]:
    ...

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 and 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 the filesystem and the glue catalog. Switching from Iceberg to regular table or vice versa is not supported.

See athena adapter for partitioning and other options.

You can also force all tables to be in iceberg format:

[destination.athena]
force_iceberg = true

You can also adjust iceberg table properties:

[destination.athena.table_properties]
vacuum_max_snapshot_age_seconds = 86400

LakeFormation tags#

AWS lakeformation tags can be set on the database level when running a pipeline. Resources/tables will inherit the same tags as their parent database. It will not affect data in staging.

Prerequisites#

• The lakeformation tags you want to apply must already exists, created through IAC or the AWS console.

• The s3 location the data lands in must be registered as a data location in lakeformation

• The IAM role/user running the pipeline is required to have lakeformation permissions in addition to regular IAM permissions

  • ASSOCIATE on the lf tags to apply
  • CREATE_TABLE/DESCRIBE/ALTER on Database with the lf tags to apply
  • ALL on Table

See full Lake Formation permission reference

Tags can be set on database level using the destination config

[destination.athena.lakeformation_config]
enabled = true

[destination.athena.lakeformation_config.tags]
my_tag = "my_key"

To remove lakeformation tags you can set:

[destination.athena.lakeformation_config]
enabled = false

merge support#

The merge write disposition is supported for Athena when using Iceberg tables.

dbt support#

Athena is supported via dbt-athena-community. Credentials are passed into aws_access_key_id and aws_secret_access_key of the 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 set up region_name in the Athena configuration below. You can also set up the table catalog name to change the default: awsdatacatalog

[destination.athena]
aws_data_catalog="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#

• Parquet is used by default.

Athena adapter#

You can use the athena_adapter to add partitioning to Athena tables. This is currently only supported for Iceberg tables.

Iceberg tables support a few transformation functions for partitioning. Info on all supported functions in the AWS documentation.

Use the athena_partition helper to generate the partitioning hints for these functions:

• athena_partition.year(column_name: str): Partition by year of date/datetime column.
• athena_partition.month(column_name: str): Partition by month of date/datetime column.
• athena_partition.day(column_name: str): Partition by day of date/datetime column.
• athena_partition.hour(column_name: str): Partition by hour of date/datetime column.
• athena_partition.bucket(n: int, column_name: str): Partition by hashed value to n buckets
• athena_partition.truncate(length: int, column_name: str): Partition by truncated value to length (or width for numbers)

Here is an example of how to use the adapter to partition a table:

from datetime import date

import dlt
from dlt.destinations.adapters import athena_partition, athena_adapter

data_items = [
    (1, "A", date(2021, 1, 1)),
    (2, "A", date(2021, 1, 2)),
    (3, "A", date(2021, 1, 3)),
    (4, "A", date(2021, 2, 1)),
    (5, "A", date(2021, 2, 2)),
    (6, "B", date(2021, 1, 1)),
    (7, "B", date(2021, 1, 2)),
    (8, "B", date(2021, 1, 3)),
    (9, "B", date(2021, 2, 1)),
    (10, "B", date(2021, 3, 2)),
]

@dlt.resource(table_format="iceberg")
def partitioned_data():
    yield [{"id": i, "category": c, "created_at": d} for i, c, d in data_items]

# Add partitioning hints to the table
athena_adapter(
    partitioned_data,
    partition=[
        # Partition per category and month
        "category",
        athena_partition.month("created_at"),
    ],
)

pipeline = dlt.pipeline("athena_example")
pipeline.run(partitioned_data)