Found a way to identify iceberg tables given that spark returns
an error when trying to execute "SHOW TABLE EXTENDED..."  See
https://issues.apache.org/jira/browse/SPARK-33393

Instead of show table extended a "DESCRIBE EXTENDED" is
performed to retrieve the provider information.  This allows
for identification of iceberg through an is_iceberg member
variable.

Allow for multiple join conditions to allow for mutliple columns to
make a row distinct

Use is_iceberg everywhere handling iceberg tables differs from other
sources of data.

[CT-276] Apache Iceberg Support dbt-labs#294

The _schema variable was used for non-iceberg tables but was being
overridden by work for iceberg v2 tables.  I've made it so the iceberg
condition will set _schema rather than blanket changing the schema for
all providers.

On second look I wasn't happy with my name choices for macro name and
method, hopefully what I have now makes more sense.

[CT-276] Apache Iceberg Support dbt-labs#294

Upon further investigation this check is not needed since
self.database will not be set.

Feature/pipeline

added missing iceberg check

_get_columns_for_catalog was not returning right info

Fix for datahub recipe

it's too slow to issue a describe table for every table in a schema

temporary fix for iceberg table discovery

…quent versions

Cccs/1.3.0 change pipeline

…rg in writes

…-yuyu

Port over Yuyu's changes from 1.6.0 to 1.3.0

…alog return is bad

Removes v1 table operations and allows throwing of exception when catalog return is bad

Bump 1.4.8

* Setuptools breaks on version 71+, force 70.1.0

* Need to run dev requirements before main

* Typo

* Try setting version directly in pipeline file for now

* Bump version to 1.4.9

* Adds partitioning branch for Python dbt models (#9)

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Co-authored-by: LoudCloudCrowd <[email protected]>

…odels