Group 4 - Power Users: Production Hardening¶

Your mission¶

You're the platform team. The other groups are building features; you're making sure the whole MediaPulse project is ready to run in production. That means auditing quality, enforcing standards, hardening tests, and designing a CI/CD pipeline that catches problems before they reach the warehouse.

This is a 2-day open hackathon. There is no prescribed order beyond the checklist steps - prioritise based on what you find.

Learning objectives¶

By the end of the hackathon you will be able to:

Run dbt-project-evaluator and interpret its output to identify structural problems
Use dbt-codegen to auto-generate YAML for undocumented sources and models
Apply dbt-expectations for statistical and distributional data quality tests
Define model contracts to enforce column-level schemas at run time
Reason about test severity (error vs warn) and when each is appropriate
Design a CI/CD pipeline using dbt Cloud jobs: slim CI, nightly full-refresh, PR validation
Articulate the difference between hard requirements (must pass before deploy) and nice-to-haves

Key tools¶

dbt-project-evaluator¶

Installs as a dbt package. Runs a suite of models that query your project's metadata and flags structural violations (missing documentation, fan-out tests, exposure gaps, etc.).

# packages.yml
packages:
  - package: dbt-labs/dbt_project_evaluator
    version: [">=0.8.0", "<1.0.0"]

dbt deps
dbt build --select package:dbt_project_evaluator

Results land in models prefixed fct_ and rpt_ - query them to see violations.

dbt-codegen¶

Generates boilerplate YAML so you don't have to write it by hand.

# Generate a source definition
dbt run-operation generate_source --args '{"schema_name": "streaming"}'

# Generate model YAML (columns + descriptions stub)
dbt run-operation generate_model_yaml --args '{"model_names": ["stg_streaming__watch_events"]}'

Paste the output into your YAML files and fill in descriptions.

dbt-expectations¶

A port of Great Expectations into dbt. Adds dozens of statistical tests beyond the four built-in generics.

models:
  - name: fct_ad_impressions
    tests:
      - dbt_expectations.expect_table_row_count_to_be_between:
          min_value: 1000
          max_value: 50000000
    columns:
      - name: impressions_count
        tests:
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              max_value: 10000000

Model contracts¶

Contracts enforce that a model's schema matches its YAML definition at run time. If a column is missing or has the wrong type, the run fails.

models:
  - name: revenue_by_content
    config:
      contract:
        enforced: true
    columns:
      - name: content_id
        data_type: varchar
        constraints:
          - type: not_null

CI/CD in dbt Cloud¶

The goal is to catch problems as early as possible:

Job	Trigger	Selector	Purpose
Slim CI	PR opened / updated	`state:modified+`	Only rebuild what changed and its downstream
Nightly full-refresh	Cron 02:00	`+` (all)	Full rebuild, catch schema drift
PR validation	PR merge	`tag:critical`	Run critical-path models + tests before merge

The slim CI job requires a deferred environment - it compares your changed models against the production manifest (manifest.json) so it knows what "state:modified" means.

Time guide¶

This is intentionally open-ended. A suggested arc:

Session	Focus
Day 1 AM	Audit with dbt-project-evaluator; triage findings
Day 1 PM	Fix highest-priority violations; add dbt-codegen YAML
Day 2 AM	dbt-expectations; model contracts; test severity review
Day 2 PM	CI/CD design; hard requirements document; prep presentation

Head to the Checklist when you're ready to start.