feat: Implement non-partitioned tmp table

[svdimchenko]

Description

Resolves: #396

Currently we have partitioned temp table creation for table and increment materialisation.
However this can lead to over price and over resources consumption as model's sql can be complicated. So that batches split run on this complicated sql may be not the very optimal way.

This PR implements the logic when we skip partitioning config while creating new tmp table and then we calculate batches and run insert statement based on this non-partitioned table.

Models used to test

table iceberg

{{
  config(
    schema='test_schema',
    materialized='table',
    partitioned_by=['DAY(date_column)', 'doy'],
    table_type='iceberg'
  )
}}

SELECT
    CAST(date_column AS DATE) as date_column,
    doy(date_column) as doy,
    random() as rnd
FROM (
    VALUES (
        SEQUENCE(FROM_ISO8601_DATE('2020-01-01'), FROM_ISO8601_DATE('2023-07-24'), INTERVAL '1' DAY)
    )
) AS t1(date_array)
CROSS JOIN UNNEST(date_array) AS t2(date_column)

increment iceberg

{{
  config(
    schema='test_schema',
    materialized='incremental',
    incremental_strategy='merge',
    partitioned_by=['DAY(date_column)', 'doy'],
    table_type='iceberg',
    unique_key=['doy']
  )
}}

select * from {{ ref('test_dbt_table') }}

table hive

{{
  config(
    schema='test_schema',
    materialized='table',
    partitioned_by=['date_column', 'doy'],
    table_type='hive'
  )
}}

SELECT
    random() as rnd,
    CAST(date_column AS DATE) as date_column,
    doy(date_column) as doy
FROM (
    VALUES (
        SEQUENCE(FROM_ISO8601_DATE('2020-01-01'), FROM_ISO8601_DATE('2023-07-24'), INTERVAL '1' DAY)
    )
) AS t1(date_array)
CROSS JOIN UNNEST(date_array) AS t2(date_column)

Checklist

• You followed contributing section
• You kept your Pull Request small and focused on a single feature or bug fix.
• You added unit testing when necessary
• You added functional testing when necessary

[nicor88]

Great work 💯 - our functional tests should cover this implementation already I believe and they seems ✅