Added packages to improve data quality of project to packages.yml

[BindusekharGorintla]

Added new dbt packages to enhance productivity, data quality, and observability:

1. dbt-labs/codegen

   • Automates repetitive SQL/model creation tasks.
   • Provides macros to generate base models and schema.yml files.
   • Speeds up development and reduces manual boilerplate.

2. calogica/dbt_expectations

   • Introduces robust data quality tests inspired by Great Expectations.
   • Enables checks for uniqueness, null values, accepted ranges, and distributions.
   • Ensures higher reliability and trust in transformed datasets.

3. elementary-data/elementary

   • Adds observability and monitoring capabilities to dbt projects.
   • Tracks source freshness, anomalies, and test results.
   • Generates reports and dashboards for proactive data issue detection.

Together, these packages strengthen the dbt workflow by:

• Accelerating model development (codegen).
• Enforcing data quality standards (dbt_expectations).
• Providing end-to-end monitoring and visibility (elementary).

Description & motivation

Checklist

• I have verified that these changes work locally
• I have updated the README.md (if applicable)
• I have added tests & descriptions to my models (and macros if applicable)
• I have run dbt test and python -m pytest . to validate existing tests

[dgitis]

You need to do something useful with those packages before I'm going to recommend merging this PR.

Just adding the packages makes it harder to use them over letting users add them to their own projects because now the user has to deal with dependency conflicts with these added packages in dbt-GA4 and in other packages that are legitimately using the new packages.

[willbryant]

calogica/dbt_expectations isn't even actively supported 😂

This just looks like AI spam, close it?