• Supports dbt version 1.0.*
• Supports Seeds
• Correctly detects views and their columns
• Support incremental models
  • Support two incremental update strategies: insert_overwrite and append
  • Does not support the use of unique_key
• Only supports Athena engine 2
  • Changing Athena Engine Versions

• pip install dbt-athena-adapter
• Or pip install git+https://github.com/Tomme/dbt-athena.git

To start, you will need an S3 bucket, for instance my-staging-bucket and an Athena database:

CREATE DATABASE IF NOT EXISTS analytics_dev
COMMENT 'Analytics models generated by dbt (development)'
LOCATION 's3://my-staging-bucket/'
WITH DBPROPERTIES ('creator'='Foo Bar', 'email'='[email protected]');

Notes:

• Take note of your AWS region code (e.g. us-west-2 or eu-west-2, etc.).
• You can also use AWS Glue to create and manage Athena databases.

This plugin does not accept any credentials directly. Instead, credentials are determined automatically based on aws cli/boto3 conventions and stored login info. You can configure the AWS profile name to use via aws_profile_name. Checkout DBT profile configuration below for details.

A dbt profile can be configured to run against AWS Athena using the following configuration:

Example profiles.yml entry:

athena:
  target: dev
  outputs:
    dev:
      type: athena
      s3_staging_dir: s3://athena-query-results/dbt/
      region_name: eu-west-1
      schema: dbt
      database: awsdatacatalog
      aws_profile_name: my-profile
      work_group: my-workgroup

Additional information

• threads is supported
• database and catalog can be used interchangeably

• external_location (default=none)
  • The location where Athena saves your table in Amazon S3
  • If none then it will default to {s3_staging_dir}/tables
  • If you are using a static value, when your table/partition is recreated underlying data will be cleaned up and overwritten by new data
• partitioned_by (default=none)
  • An array list of columns by which the table will be partitioned
  • Limited to creation of 100 partitions (currently)
• bucketed_by (default=none)
  • An array list of columns to bucket data
• bucket_count (default=none)
  • The number of buckets for bucketing your data
• format (default='parquet')
  • The data format for the table
  • Supports ORC, PARQUET, AVRO, JSON, or TEXTFILE
• write_compression (default=none)
  • The compression type to use for any storage format that allows compression to be specified. To see which options are available, check out CREATE TABLE AS
• field_delimiter (default=none)
  • Custom field delimiter, for when format is set to TEXTFILE

More information: CREATE TABLE AS

Support for incremental models:

• Support two incremental update strategies with partitioned tables: insert_overwrite and append
• Does not support the use of unique_key

Due to the nature of AWS Athena, not all core dbt functionality is supported. The following features of dbt are not implemented on Athena:

• Snapshots

• Quoting is not currently supported

  • If you need to quote your sources, escape the quote characters in your source definitions:

  version: 2
  
  sources:
    - name: my_source
      tables:
        - name: first_table
          identifier: "first table"       # Not like that
        - name: second_table
          identifier: "\"second table\""  # Like this

• Tables, schemas and database should only be lowercase

• Only supports Athena engine 2

  • Changing Athena Engine Versions

First, install the adapter and its dependencies using make (see Makefile):

make install_deps

Next, configure the environment variables in dev.env to match your Athena development environment. Finally, run the tests using make:

make run_tests

• fishtown-analytics/dbt
• fishtown-analytics/dbt-presto
• Dandandan/dbt-athena
• laughingman7743/PyAthena