dbt

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dbt Integration

Honeydew dbt integration allows to materialize Honeydew-managed datasets via dbt.

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Usage

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Dynamic Datasets in dbt

Given a dynamic dataset named my_dataset_name create a dbt model called my_dataset_name.sql.

Put the following in the model code:

-- Optional: Set up various materialization parameters
{{ config(materialized='table') }}

-- Set up any additional dependencies in dbt with
-- depends_on: {{ ref('upstream_parent_model') }}

-- Dynamic dataset materialization
{{ honeydew.get_dataset_sql('my_dataset_name') }}

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Setup

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Installation

Honeydew runs in dbt projects connected to Snowflake. To interact with Honeydew, dbt can call the Honeydew Native Application.

Install the Honeydew dbt package by adding to packages.yml of your dbt project:

packages:
  - git: "https://github.com/honeydew-ai/honeydew-dbt.git"
    revision: "latest"

Run dbt deps to apply.

Set the following variables in the dbt project configuration (dbt_project.yml):

An example dbt_project.yml configuration:

vars:
  "honeydew:workspace": "tpch_demo"
  "honeydew:branch": "prod"
  "honeydew:snowflake_native_app_name": "SEMANTIC_LAYER_ENTERPRISE_EDITION"

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Metadata Sync

Honeydew dbt metadata sync supports syncing with dbt the following metadata:

• Source dbt model references (when source dbt model is set for entities)
• Column descriptions
• Labels (as tags)
• General purpose metadata (if set)

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Honeydew and different dbt offerings

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dbt Core

Honeydew provides a dbt package that integrates with dbt. Using that package, Honeydew can manage dbt models in dbt Core.

Running dbt is based on an external orchestrator such as Airflow.

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dbt Cloud

Works in the same way as dbt Core without limitations. There is no need for an external orchestrator. Additional integration is available to link dbt documentation built on a dbt Cloud job and the Honeydew UI .

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dbt Semantic Layer

There is no need to use the dbt Semantic Layer when using Honeydew. Honeydew metrics can connect to any BI tool directly. There are a number of core differences:

1. Composable metrics: Honeydew enables a high degree of metric composition, including applying filters, partial aggregations, time-based logic, etc.
2. Multi-Join metrics: Honeydew metrics can include multi-hop joins.
3. Level of detail metrics: Honeydew metrics can include partial level of details.
4. Using metrics as filters: Honeydew metrics can be used to filter data.
5. Use metrics in data marts: Honeydew metrics can be used directly to build columns in other tables.
6. BI connectivity and Metadata Sync: Honeydew has a standard integration that works with all major BI tools.

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FAQ