sqlfluff VS dbt-utils

Repo : https://github.com/sqlfluff/sqlfluff

I'm surprised sqlfluff hasn't been mentioned yet. Perhaps not a comprehensive list, but it's worked for everything I've thrown at it. There's an ANSI keyword list [0], and then dialect-specific lists for everything from DB2 [1] to Snowflake [2].

[0]: https://github.com/sqlfluff/sqlfluff/blob/main/src/sqlfluff/...

It has tons of annoying quirks, but I couldn't imagine running a DBT project without it: https://github.com/sqlfluff/sqlfluff

Move SQL queries to sql files and read from files (Use sqlfluff to lint the code https://github.com/sqlfluff/sqlfluff)

SQLFluff is a good CLI tool for this and includes support for jinja and dbt. I don't think there's a VSCode plugin for it yet.

This linter can really enforce some best practices https://github.com/sqlfluff/sqlfluff

A list of best practices:

BigQuery SQL and SQLFluff

There's a SQL parser/linter called SQLFluff that my team uses for our CI/CD. I've made a few pull requests to fix the parser for the particular SQL dialect we used, and my college compiler classes definitely helped.

// To get around this, we can use the approach outlined by how dbt does ansi sql generate_series

  // https://github.com/dbt-labs/dbt-utils/blob/main/macros/sql/generate_series.sql

I got bit by dbt-utils/deduplicate naively removing any row that contained a null in it recently, but fortunately there was a workaround for Databricks and a few other flavors of SQL.

I'm used to utilising dbt and defining my tests there (along with dbt-utils or https://github.com/calogica/dbt-expectations): I simply add a list item to a column definition and can already define a great number of tests without having to copy code. I can even extend the pre-defined using generic tests. Writing custom tests also integrates nicely. Additionally it's very convenient to tag tests or define a severity. The learning curve for a business engineer is almost flat as long as they know some SQL.

My top three:

- Dev/stag/prod env check numbers before pushing to production.

- Unions between two sources that are not the same shape can be done without the headache. https://github.com/dbt-labs/dbt-utils#union_relations-source

- Macros for common case when statements.

Add tests: unit tests in SQL are still not really practical, but testing the data, before allowing users to see it, is possible. dbt has some basic tests like Non-NULL and so on. dbt_utils supports comparing data across tables. If you need more, there is Great Expectation and similar tools. dbt also supports writing SQL queries which output “bad” rows. Use this to, e.g. check a specific order against manually checked correct data. Tests give you confidence that your pipelines produce correct results: nothing is worse than waking up with a Slack message from your boss that the graphs look wrong… They are especially useful in case you have to refactor a data pipeline. Basically every query you would run during the QA phase of a change request has a high potential to become an automatic test.

The equality test macro is also in the dbt-utils package from fishtown at https://github.com/fishtown-analytics/dbt-utils/blob/master/macros/schema_tests/equality.sql