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@@ -4,6 +4,118 @@ Real-world examples showing what altimate can do across data engineering workflo
---
+## Onboarding a Junior Data Engineer — Automated Peer Review
+
+`dbt` `SQL Quality` `A-F Grading`
+
+A junior engineer always gets senior-level peer review — altimate grades the mart layer, flags every anti-pattern, fixes the offending SQL, and re-grades to prove the improvement.
+
+**Prompt:**
+
+> Run quality checks and grading on the mart layer queries of my dbt project to find out the SQL anti patterns. Also fix those issues, validate and re-grade them.
+
+[](https://us06web.zoom.us/clips/share/aJ5crYpRT4WSH6crh4iwcA)
+
+*Click on the image to watch the demo.*
+
+---
+
+## An Upstream Schema Changed. What Just Broke?
+
+`dbt` `Lineage` `Incident Response`
+
+A staging model renames a column and the dbt run fails. altimate reads the error log, traces lineage, pinpoints every broken downstream model, and fixes the references — no hunting through dozens of files.
+
+**Prompt:**
+
+> Switch to the `bug/column-rename` branch. A staging model was updated to rename a column, but downstream models weren't updated. Check the error log at `logs/dbt_run_error.log` and identify all the downstream models that broke. Fix them.
+
+[](https://us06web.zoom.us/clips/share/AZcsChIhRF6Mwk4Sl6QaCA)
+
+*Click on the image to watch the demo.*
+
+---
+
+## Column-Level Lineage Diff for PR Reviews
+
+`dbt` `Column-Level Lineage` `PR Review`
+
+Before approving a risky refactor PR, get a precise column-level lineage diff between the two branches — new dependencies, removed ones, source changes — so you know the blast radius before you merge.
+
+**Prompt:**
+
+> I am reviewing a PR that refactors `mart_patient_360` — the PR claims to fix a cartesian explosion, hash SSN for HIPAA, and add financial metrics. Before I approve, I need to understand exactly what changed at the column-level data flow. Run a column-level lineage diff between the old version (on `main`) and the new version (on `refactor/mart-patient-360-fix-cartesian-and-pii`) of `models/marts/mart_patient_360.sql`. Show me which column dependencies were added, removed, or changed source — I want to know the blast radius before merging.
+
+[](https://youtu.be/SpUWKq7x_T0)
+
+*Click on the image to watch the demo.*
+
+---
+
+## From Idea to Production dbt Model in One Terminal Session
+
+`dbt` `Model Scaffolding` `Tests & Docs`
+
+A plain-English analytics request becomes a production-ready dbt asset — SQL, documentation, schema tests, validation — all in a single session.
+
+**Prompt:**
+
+> I need a new dbt model called `mart_monthly_revenue`. It should show monthly revenue broken down by merchant risk tier. Include total revenue, transaction count, unique merchants, and average transaction value. Use `stg_transactions` and `stg_merchants` as the upstream models.
+
+[](https://us06web.zoom.us/clips/share/caXUfjyqQmqdylPQzao86A)
+
+*Click on the image to watch the demo.*
+
+---
+
+## Refactoring dbt Models Without Breaking Everything
+
+`dbt` `Refactor` `Impact Analysis`
+
+Plan a schema refactor with a complete blast-radius report before merging — every downstream model that needs a change, and exactly what change it needs.
+
+**Prompt:**
+
+> Switch to the `feat/refactor-stg-transactions` branch. The `stg_transactions` model renames `created_at` to `transaction_at` and drops `card_last_four` and `ip_address`. Before I merge this, tell me every downstream model that will break and what changes each one needs.
+
+[](https://us06web.zoom.us/clips/share/-I9EE6LAQguh0tS1oLG6aA)
+
+*Click on the image to watch the demo.*
+
+---
+
+## Migrating From MS SQL Server to MS Fabric via dbt
+
+`MS SQL Server` `MS Fabric` `dbt` `Migration` `data-diff`
+
+End-to-end warehouse migration: review the stored procedures, generate dbt models targeting Fabric, run cross-database data-diff validation, schema-difference checks, build the project, and produce an interactive migration validation dashboard.
+
+**Prompt:**
+
+> We need to perform MS SQL Server to MS Fabric migration via dbt. We have SQL Server code present at `sql_server_objects/stored_procedures` — review it and create dbt models for the same with Fabric as the target, following `migration_best_practices`. Raw layer tables are already populated in Fabric. Once done, perform compilation, cross-database `data_diff` validation between existing and new code, a schema-difference check, and then build the project in Fabric. Finally, produce an interactive migration validation dashboard with migration status, validation results, lineage, etc.
+
+[](https://us06web.zoom.us/clips/share/4intYUDRQ2Wo7C42qIeJig)
+
+*Click on the image to watch the demo.*
+
+---
+
+## A Platform Admin's First Day With Microsoft Fabric
+
+`MS Fabric` `Governance` `Lineage` `PII Audit`
+
+Drop altimate into an unfamiliar Fabric instance and get an immediate picture — lineage, code quality, active roles, users, PII exposures — in one prompt.
+
+**Prompt:**
+
+> I am new to the Microsoft Fabric instance. Show me the lineage, code quality, active roles, users, PII exposures etc.
+
+[](https://us06web.zoom.us/clips/share/Fh00mmQnRKOR6oxjQf6utA)
+
+*Click on the image to watch the demo.*
+
+---
+
## NYC Taxi Coverage Dashboard
`DuckDB` `dbt` `Airflow` `Python`