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Vale: test with several files #5934

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Vale: test with several files #5934

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@nghi-ly nghi-ly commented Aug 15, 2024

What are you changing in this pull request and why?

Testing linter with several files

@nghi-ly nghi-ly requested review from dataders and a team as code owners August 15, 2024 20:38
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@github-actions github-actions bot added content Improvements or additions to content developer blog This content fits on the developer blog. guides Knowledge best suited for Guides size: large This change will more than a week to address and might require more than one person Docs team Authored by the Docs team @dbt Labs labels Aug 15, 2024
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this test with many edited files is a bit interesting, @mirnawong1 .

  • Vale didn't flag anything on 2 (out of 3) files. i expected for some lines to be flagged. i wonder if this has something to do with "reviewdog" erroring out.
  • Vale flagged several lines in the 3rd file but there were some false positives.


## System of a noun: deciding what happens where
## Fabric Best Practices
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hmm, i expected Vale to flag this line but it didn't

models will still query from the same raw data source in Snowflake. By using `source`, you can
test and document your raw data and also understand the lineage of your sources.

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End of file. Vale didn't flag anything for this file, which seems unusual. i expected at least a "typo" warning.

the `range` dict to generate the partitioning clause for the table.

<Tabs
defaultValue="source"
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our custom components shouldn't get flagged as typos. is it possible to ignore these or maybe add these to our custom dictionary?

BigQuery supports an [older mechanism of partitioning](https://cloud.google.com/bigquery/docs/partitioned-tables#ingestion_time) based on the time when each row was ingested. While we recommend using the newer and more ergonomic approach to partitioning whenever possible, for very large datasets, there can be some performance improvements to using this older, more mechanistic approach. [Read more about the `insert_overwrite` incremental strategy below](#copying-ingestion-time-partitions).

dbt will always instruct BigQuery to partition your table by the values of the column specified in `partition_by.field`. By configuring your model with `partition_by.time_ingestion_partitioning` set to `True`, dbt will use that column as the input to a `_PARTITIONTIME` pseudocolumn. Unlike with newer column-based partitioning, you must ensure that the values of your partitioning column match exactly the time-based granularity of your partitions.

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"time-based" shouldn't be flagged as a typo

i see another instances of this on this file

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Vale linting found issues. Please check the 'Files change' tab for detailed results.

Link to detailed report: Files changed

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Vale linting found issues. Please check the 'Files change' tab for detailed results.

Link to detailed report: Files changed

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Vale linting found issues. Please check the 'Files change' tab for detailed results.

Link to detailed report: Files changed

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❗️Oh no, some Vale linting found issues! Please check the 'Files change' tab for detailed results and make the necessary updates ✨.

➡️ Link to detailed report: Files changed

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❗️Oh no, some Vale linting found issues! Please check the Files change tab for detailed results and make the necessary updates.

➡️ Link to detailed report: Files changed

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