- Source config extension for metadata about external file structure
- Adapter macros to create external tables and refresh external table partitions
- Snowflake-specific macros to create, backfill, and refresh snowpipes
# iterate through all source nodes, create if missing + refresh if appropriate
$ dbt run-operation stage_external_sources
# iterate through all source nodes, create or replace + refresh if appropriate
$ dbt run-operation stage_external_sources --vars 'ext_full_refresh: true'
The macros assume that you have already created an external stage (Snowflake) or external schema (Redshift/Spectrum), and that you have permissions to select from it and create tables in it.
The stage_external_sources
macro accepts a similar node selection syntax to
snapshotting source freshness.
# Stage all Snowplow and Logs external sources:
$ dbt run-operation stage_external_sources --args 'select: snowplow logs'
# Stage a particular external source table:
$ dbt run-operation stage_external_sources --args 'select: snowplow.event'
Maybe someday:
$ dbt source stage-external
$ dbt source stage-external --full-refresh
$ dbt source stage-external --select snowplow.event logs
version: 2
sources:
- name: snowplow
tables:
- name: event
# NEW: "external" property of source node
external:
location: # S3 file path or Snowflake stage
file_format: # Hive specification or Snowflake named format / specification
row_format: # Hive specification
table_properties: # Hive specification
# Snowflake: create an empty table + pipe instead of an external table
snowpipe:
auto_ingest: true
aws_sns_topic: # AWS
integration: # Azure
copy_options: "on_error = continue, enforce_length = false" # e.g.
# Specify a list of file-path partitions.
# ------ SNOWFLAKE ------
partitions:
- name: collector_date
data_type: date
expression: to_date(substr(metadata$filename, 8, 10), 'YYYY/MM/DD')
# ------ REDSHIFT -------
partitions:
- name: appId
data_type: varchar(255)
vals: # list of values
- dev
- prod
path_macro: dbt_external_tables.key_value
# Macro to convert partition value to file path specification.
# This "helper" macro is defined in the package, but you can use
# any custom macro that takes keyword arguments 'name' + 'value'
# and returns the path as a string
# If multiple partitions, order matters for compiling S3 path
- name: collector_date
data_type: date
vals: # macro w/ keyword args to generate list of values
macro: dbt.dates_in_range
args:
start_date_str: '2019-08-01'
end_date_str: '{{modules.datetime.date.today().strftime("%Y-%m-%d")}}'
in_fmt: "%Y-%m-%d"
out_fmt: "%Y-%m-%d"
path_macro: dbt_external_tables.year_month_day
# Specify ALL column names + datatypes. Column order matters for CSVs.
# Other file formats require column names to exactly match.
columns:
- name: app_id
data_type: varchar(255)
description: "Application ID"
- name: platform
data_type: varchar(255)
description: "Platform"
...
sample_sources
for full valid YML config that establishes Snowplow events as a dbt source and stage-ready external table in Snowflake and Spectrum.sample_analysis
for a "dry run" version of the DDL/DML thatstage_external_sources
will run as an operation
- Redshift (Spectrum)
- Snowflake
- TK: Spark