title: Using external parquet tables in a DBT pipeline
body: <p>I'm trying to set up a simple DBT pipeline that uses a parquet tables stored on Azure Data Lake Storage and creates another tables that is also going to be stored in the same location.</p>
  <p>Under my <code>models/</code> (which is defined as my sources path) I have 2 files <code>datalake.yml</code> and <code>orders.sql</code>. <code>datalake.yml</code> looks like this:</p>
  <pre><code>version:2
  sources:
     - name: datalake
       tables:
          - name: customers
            external:
               location: path/to/storage1 # I got this by from file properties in Azure
               file_format: parquet
            columns:
               - name: id
                 data_type: int
                 description: &quot;ID&quot;
               - name: ...
  </code></pre>
  <p>My <code>orders.sql</code> table looks like this:</p>
  <pre><code>{{config(materialized='table', file_format='parquet', location_root='path/to/storage2')}}
  select name, age from {{ source('datalake', 'customers') }}
  </code></pre>
  <p>I'm also using the <code>dbt-external-tables</code> package. Also note that when I run <code>dbt debug</code> everything is fine and I can connect to my database (which happens to be Databricks).</p>
  <p>I tried running <code>dbt run-operation stage_external_sources</code> which returns <code>Error: staging external sources is not implemented for the default adapter</code>. When I run <code>dbt run</code>, I get <code>Error: UnresolvedRelation datalake.customers</code>.</p>
  <p>Or perhaps I could make use of the hive metastore instead somehow? Any tips on how I could fix this would be highly appreciated!</p>