dbt-athena-community 1.3.1

The athena adapter plugin for dbt (data build tool)

dbt-athena

• Supports dbt version 1.3.*
• 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
• Does not support Python models

Installation

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

Prerequisites

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'='[e-mail]');

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.

Credentials

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.

Configuring your profile

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

Option	Description	Required?	Example
s3_staging_dir	S3 location to store Athena query results and metadata	Required	s3://bucket/dbt/
region_name	AWS region of your Athena instance	Required	eu-west-1
schema	Specify the schema (Athena database) to build models into (lowercase only)	Required	dbt
database	Specify the database (Data catalog) to build models into (lowercase only)	Required	awsdatacatalog
poll_interval	Interval in seconds to use for polling the status of query results in Athena	Optional	5
aws_profile_name	Profile to use from your AWS shared credentials file.	Optional	my-profile
work_group	Identifier of Athena workgroup	Optional	my-custom-workgroup
num_retries	Number of times to retry a failing query	Optional	3

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

Usage notes

Models

Table Configuration

• 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
• table_properties: table properties to add to the table, valid for Iceberg only
• strict_location (default=True): when working with iceberg it's possible to rename tables, in order to do so, tables need to avoid to have same location. Setting up strict_location to false allow a table creation on an unique location

More information: CREATE TABLE AS

Supported functionality

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

Iceberg

The adapter support table materialization for Iceberg.

To get started just add this as your model:

{{ config(
    materialized='table',
    format='iceberg',
    partitioned_by=['bucket(5, user_id)'],
    strict_location=false,
    table_properties={
    	'optimize_rewrite_delete_file_threshold': '2'
    	}
) }}

SELECT
	'A' AS user_id,
	'pi' AS name,
	'active' AS status,
	17.89 AS cost,
	1 AS quantity,
	100000000 AS quantity_big,
	current_date AS my_date

Iceberg support bucketing as hidden partitions, therefore use the partitioned_by config to add specific bucketing conditions.

Known issues

• 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

Contributing

This connector works with Python from 3.7 to 3.10.

Getting started

In order to start developing on this adapter clone the repo and run this make command (see Makefile) :

make setup

It will :

1. Install all dependencies.
2. Install pre-commit hooks.

Next, configure the environment variables in dev.env to match your Athena development environment.

Running tests

You can run the tests using make:

make run_tests

Community

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