diff --git a/.github/workflows/portfolio-parse.yml b/.github/workflows/portfolio-parse.yml
new file mode 100644
index 0000000..3ebfc93
--- /dev/null
+++ b/.github/workflows/portfolio-parse.yml
@@ -0,0 +1,42 @@
+name: Parse portfolio dbt project
+
+on:
+  pull_request:
+    branches: [main]
+  push:
+    branches: [main]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  parse:
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    defaults:
+      run:
+        working-directory: task-2
+    env:
+      DATABRICKS_HOST: offline.invalid
+      DATABRICKS_HTTP_PATH: /sql/1.0/warehouses/offline
+      DATABRICKS_TOKEN: offline-placeholder
+      DBT_SCHEMA: portfolio_parse
+      DBT_SEND_ANONYMOUS_USAGE_STATS: 'false'
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v6
+        with:
+          version: '0.8.22'
+          python-version: '3.12'
+          enable-cache: true
+      - name: Install original locked environment
+        run: uv sync --frozen
+      - name: Prepare dummy profile outside the checkout
+        run: |
+          mkdir -p "$RUNNER_TEMP/portfolio-dbt-profile"
+          cp profiles.yml.example "$RUNNER_TEMP/portfolio-dbt-profile/profiles.yml"
+      - name: Install dbt package and parse without warehouse access
+        run: |
+          uv run dbt deps --profiles-dir "$RUNNER_TEMP/portfolio-dbt-profile"
+          uv run dbt parse --profiles-dir "$RUNNER_TEMP/portfolio-dbt-profile" --no-partial-parse
diff --git a/.gitignore b/.gitignore
index 03c790e..6451516 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,6 +16,7 @@ logs/
 **/partial_parse.msgpack
 
 # Python
+.venv/
 __pycache__/
 *.py[cod]
 *$py.class
diff --git a/AI_ASSIST.md b/AI_ASSIST.md
index b134b96..cbe142a 100644
--- a/AI_ASSIST.md
+++ b/AI_ASSIST.md
@@ -4,13 +4,63 @@ Record at least one point where you used an AI coding assistant (ChatGPT, Claude
 
 ## Interaction 1
 
-- **Tool used:** (e.g. ChatGPT / Cursor / Claude)
-- **Task / Problem:** (e.g. debugging dbt connection profile / writing PySpark join / configuring Job trigger)
+- **Tool used:** ChatGPT
+- **Task / Problem:** Debugging the Databricks HTTP path being incorrectly converted by Git Bash.
 - **Prompt sent:**
-  > `___`
+  > `i got this error
+
+Connection:
+00:21:33 host: **\*\*\*\***\*\***\*\*\*\***\***\*\*\*\***\*\***\*\*\*\***
+00:21:33 http_path: C:/Program Files/Git/sql/1.0**\*\***\*\*\*\***\*\***
+00:21:33 catalog: hyf
+00:21:33 schema: dev_mohammedalfakih
+00:21:33 Registered adapter: databricks=1.12.3
+00:21:34 Databricks adapter: SPOG host (host_type='workspace'): no
+00:21:34 Databricks adapter: workspace_id (from ?o= in http_path): None
+00:21:34 Databricks adapter: databricks-sql-connector version: 4.3.0 (supported)
+00:21:34 Databricks adapter: databricks-sdk version: 0.117.0 (supported)
+00:21:34 databricks-sql-connector adapter: ThriftBackend.attempt_request: Exception: %s
+ERROR:databricks.sql.backend.thrift_backend:ThriftBackend.attempt_request: Exception:
+00:21:34 Databricks adapter: Connection(session-id=Unknown) - Exception while trying to create connection: Error during request to server.
+Error properties: attempt=1/30, bounded-retry-delay=None, elapsed-seconds=0.10899758338928223/900.0, error-message=, http-code=404, method=OpenSession, no-retry-reason=non-retryable error, original-exception=, query-id=None, session-id=None
+00:21:34 Connection test: [ERROR]
+
+00:21:34 1 check failed:
+00:21:34 dbt was unable to connect to the specified database.
+The database returned the following error:
+
+> Database Error
+> Database Error
+
+    Error during request to server.
+
+Check your database credentials and try again. For more information, visit:
+https://docs.getdbt.com/docs/configure-your-profile
+
+---
+
+i notice that http path is loaded in wrong way. i have if in my .env as DATABRICKS_HTTP_PATH=/sql/1.0/warehouses/**\*\*\*\***\*\*\***\*\*\*\***
+but in the error i can see http_path: C:/Program Files/Git/sql/1.0**\*\***\*\*\*\***\*\***
+can u explain why is this happing and how to solve it.
+note: the(\*) is only to hide secrets so I have an real values in my .env`
+
 - **Output provided by AI:**
-  > `___`
+  > `You found the cause correctly. Git Bash uses MSYS2, which automatically converts environment-variable values that look like Unix paths before passing them to native Windows programs. Therefore:
+/sql/1.0/warehouses/...
+was incorrectly converted to:
+C:/Program Files/Git/sql/1.0/warehouses/...
+That produced the HTTP 404. MSYS2 documents this environment-variable path conversion.
+Keep the value in .env unchanged—it is correct.
+Next step only
+In the same Git Bash session, run:
+export MSYS2_ENV_CONV_EXCL=DATABRICKS_HTTP_PATH
+uv run dbt debug --profiles-dir .
+This tells Git Bash not to convert that specific environment variable. The debug output should now show an HTTP path beginning with:
+/sql/1.0/warehouses/
+and ideally finish with:
+All checks passed!
+`
 - **What I kept, changed, or rejected, and why:**
-  > `___`
+  > `I kept the suggested `MSYS2_ENV_CONV_EXCL=DATABRICKS_HTTP_PATH`fix because it prevented Git Bash from converting the Databricks HTTP path, and`dbt debug`then passed. I kept the correct`/sql/1.0/warehouses/...` value unchanged and did not include any real token or credential in the AI prompt.`
 
-*(Ensure no personal passwords, Databricks tokens, or unapproved credentials are included in prompts or logged outputs.)*
+_(Ensure no personal passwords, Databricks tokens, or unapproved credentials are included in prompts or logged outputs.)_
diff --git a/README.md b/README.md
index fd4900b..15c03e4 100644
--- a/README.md
+++ b/README.md
@@ -1,74 +1,81 @@
-# HackYourFuture Data Track — Week 13 Assignment
-
-**Databricks Lab:** PySpark exploration, dbt incremental models on Delta Lake, and Git-backed Job scheduling.
-
-Full instructions live in the curriculum: [Week 13 Assignment](https://www.notion.so/hackyourfuture/Assignment-2af50f64ffc98112b371c42a3f469749).
-
-## Where to start
-
-| Folder / File | What to submit | Points (autograder) |
-| --- | --- | --- |
-| `task-1/` | PySpark notebook (`show()` on aggregated results, PySpark-vs-dbt note) | 25 |
-| `task-2/` | Ported dbt project + `WRITEUP.md` (timings + incremental explanation + `DESCRIBE HISTORY`) | 30 |
-| `task-3/` | Git-backed Databricks Job + screenshots + `SCHEDULING.md` (Jobs vs Airflow) | 15 |
-| `AI_ASSIST.md` | Documented LLM usage (prompt, tool, kept/discarded rationale) | 15 |
-| Required files | Presence of all required files across `task-1/`, `task-2/`, `task-3/`, and `AI_ASSIST.md` | 15 |
-| Secrets hygiene | No committed secrets (`profiles.yml`, `.env`, tokens) | Blocker if violated |
-
-**Passing score:** 60/100 on the autograder. Your teacher also reviews quality against the rubric (incremental config, `>` boundary, tool-choice writing, Job configuration).
-
-## Repository layout
-
-```text
-data-assignment-week-13/
-├── task-1/
-│   └── pyspark_exploration.ipynb    # or .py export from Databricks
-├── task-2/
-│   ├── dbt_project.yml              # your ported Week 10 project
-│   ├── models/                      # your dbt models
-│   ├── profiles.yml.example
-│   └── WRITEUP.md                   # incremental build write-up + DESCRIBE HISTORY
-├── task-3/                          # Git-backed Job scheduling
-│   ├── SCHEDULING.md                # Jobs vs Airflow write-up + Job Run URL
-│   └── screenshots/                 # Job config, green run, paused trigger
-├── task-4/                          # optional bonuses only (create if needed)
-├── .env.example
-├── AI_ASSIST.md                     # LLM interaction log
-└── README.md
-```
+# NYC Taxi Databricks Lakehouse
 
-## Setup
+A Databricks lab exploring yellow-taxi data with PySpark, building a daily borough mart with dbt and Delta Lake, and running dbt from a Git-backed Databricks Job.
 
-```bash
-cp .env.example .env          # fill in Databricks connection values
-cd task-2
-cp profiles.yml.example profiles.yml
-export $(grep -v '^#' ../.env | xargs)   # or source manually
-dbt debug
+**Questions:** Which pickup borough has the most records? How does the average total charge vary by payment type? How can a daily analytics table be updated with an incremental `MERGE`?
+
+My completed HackYourFuture Data Track Week 13 work, with the original notebook, transformation logic and job evidence preserved. [Project provenance](docs/provenance.md) · [Original assignment guide](docs/hyf-assignment.md).
+
+## Data flow
+
+```mermaid
+flowchart LR
+    R[Provided yellow-taxi raw trips] --> P[PySpark exploration]
+    Z[Provided taxi zone lookup] --> P
+    R --> T[dbt stg_trips view]
+    Z --> S[dbt stg_zones view]
+    T --> F[Delta daily borough mart]
+    S --> F
+    G[Git-backed Databricks Job] -. dbt deps + dbt build .-> F
+    F --> Q[Uniqueness, null and tip-ratio checks]
 ```
 
-Use Python 3.11 or 3.12 for dbt if `dbt debug` crashes on import (`uvx --python 3.11 --from dbt-databricks dbt debug`).
+**Stack:** Databricks · PySpark · SQL · dbt · Delta Lake · Databricks Jobs
+
+## Explore the completed work
+
+| Component | What to inspect |
+|---|---|
+| [PySpark notebook](task-1/pyspark_exploration.ipynb) | Zone join, borough counts, payment-type averages and saved aggregate outputs |
+| [dbt project](task-2/) | Two staging views, the incremental daily mart, safe-division macro and data checks |
+| [Incremental design](task-2/WRITEUP.md) | Merge key, date boundary, recorded timings and Delta history |
+| [Job scheduling](task-3/SCHEDULING.md) | Git source, successful historical run, paused trigger and Jobs/Airflow comparison |
+
+The notebook and dbt source definitions read `hyf.nyc_yellow.raw_trips` and `hyf.nyc_yellow.raw_zones`. Those normalized raw tables are provided by the course workspace; this repository does not ingest public TLC files or provision those tables. Their actual current coverage must be checked in the workspace.
+
+### Saved notebook results
+
+| Question | Recorded result |
+|---|---|
+| Highest pickup-borough count after the zone join | Manhattan: 112,028,489 records |
+| Average `total_amount`, payment type 1 | $30.00 |
+| Average `total_amount`, payment type 2 | $23.75 |
+
+These are outputs saved in the original coursework notebook, not a new run or current city-wide statistics. Notebook queries use the raw population; the dbt mart applies staging filters, so their totals are not directly comparable.
 
-## Check your score locally
+### Historical job evidence
 
-```bash
-bash .hyf/test.sh
-cat .hyf/score.json
+![Successful Databricks coursework job run](task-3/screenshots/job_run_success.png)
+
+The screenshot records a successful manual run on **30 July 2026**, including one incremental model, three passing data tests and one warning-level tip-ratio test. It is historical evidence, not a live deployment status. [Configuration, Git source and paused schedule screenshots](task-3/SCHEDULING.md).
+
+## Model grain and incremental behavior
+
+The model retains the assignment name **`fct_trips`**, but its grain is **one pickup borough per pickup date**. Its Delta merge key is `(pickup_borough, pickup_date)`; it is not a trip-level table. Keeping the name preserves the existing job command and coursework evidence.
+
+It produces `trip_count`, `total_fare`, `avg_tip_pct` and `avg_trip_distance`. Staging excludes missing pickup IDs and negative/NULL fares; an inner zone join excludes unmatched pickup IDs. [Metric definitions and quality limits](docs/metrics.md).
+
+The incremental filter selects dates strictly newer than the maximum date already in the target. This demonstrates an append-only daily load: late records, changes to prior days and additional records on the latest loaded day are skipped. A full refresh reprocesses them. [Current behavior and a future lookback design](task-2/WRITEUP.md).
+
+## Setup and review
+
+Requirements for a warehouse run: a Databricks workspace, SQL warehouse, access to the two normalized source tables, and permission to create views/tables in your own target schema. Python 3.12 and [uv](https://docs.astral.sh/uv/) install the existing locked local dbt environment.
+
+From the repository root:
+
+```sh
+cd task-2
+uv sync --frozen --python 3.12
 ```
 
-## Scoring ladder (autograder)
+Follow the [setup guide](docs/setup.md) to create an ignored local profile, set connection variables and build the upstream staging views plus the mart. An offline `dbt parse` review is also available without a Databricks account. Parsing validates project structure; it does not execute SQL or reproduce cloud results.
+
+## Validation and scope
 
-| Score | What the grader checks |
-| --- | --- |
-| 15 | Required files present (`task-1` notebook, `task-2/dbt_project.yml`, `WRITEUP.md`, `SCHEDULING.md`, `AI_ASSIST.md`) |
-| 25 | Task 1 notebook mentions `show` and borough/payment_type work |
-| 30 | Task 2 has incremental config (`materialized='incremental'`, `merge`, `unique_key`) and a filled `WRITEUP.md` |
-| 15 | Task 3 has screenshots in `task-3/screenshots/`, Job Run URL, and a filled `SCHEDULING.md` |
-| 15 | `AI_ASSIST.md` contains documented prompt and rationale |
-| Pass | Secrets hygiene (no committed `.env` / `profiles.yml` / `dapi` tokens) |
+Existing HYF checks are static coursework checks. The portfolio workflow additionally installs the lockfile and parses the dbt project with dummy connection settings. Neither CI path runs Databricks compute.
 
-Governance and streaming bonuses are teacher-reviewed only; they do not affect the autograder score.
+The original implementation is a learning project: source loading, late-data correction, production alerting and credential management are outside its scope. The job trigger was paused in the saved screenshot. The recorded full/incremental timings are a single historical pair, not a performance benchmark. [Validation scope](docs/validation.md).
 
-## Instructor / track maintainer
+## Credits
 
-This repo is the Week 13 student scaffold. Teacher rubric: `week_13__assignment_rubric.md` in the [datatrack](https://github.com/HackYourFuture/datatrack) curriculum repo (not shared with students).
+Author: [Mohammed Alfakih](https://github.com/mohammedalfakih-dev). Built through the [HackYourFuture](https://www.hackyourfuture.net/) Data Track using its [Week 13 assignment scaffold](https://github.com/HackYourAssignment/c55-data-week-13). Taxi data is supplied through the course's Databricks tables; public NYC taxi datasets are published by the [NYC Taxi & Limousine Commission](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page).
diff --git a/docs/hyf-assignment.md b/docs/hyf-assignment.md
new file mode 100644
index 0000000..fd4900b
--- /dev/null
+++ b/docs/hyf-assignment.md
@@ -0,0 +1,74 @@
+# HackYourFuture Data Track — Week 13 Assignment
+
+**Databricks Lab:** PySpark exploration, dbt incremental models on Delta Lake, and Git-backed Job scheduling.
+
+Full instructions live in the curriculum: [Week 13 Assignment](https://www.notion.so/hackyourfuture/Assignment-2af50f64ffc98112b371c42a3f469749).
+
+## Where to start
+
+| Folder / File | What to submit | Points (autograder) |
+| --- | --- | --- |
+| `task-1/` | PySpark notebook (`show()` on aggregated results, PySpark-vs-dbt note) | 25 |
+| `task-2/` | Ported dbt project + `WRITEUP.md` (timings + incremental explanation + `DESCRIBE HISTORY`) | 30 |
+| `task-3/` | Git-backed Databricks Job + screenshots + `SCHEDULING.md` (Jobs vs Airflow) | 15 |
+| `AI_ASSIST.md` | Documented LLM usage (prompt, tool, kept/discarded rationale) | 15 |
+| Required files | Presence of all required files across `task-1/`, `task-2/`, `task-3/`, and `AI_ASSIST.md` | 15 |
+| Secrets hygiene | No committed secrets (`profiles.yml`, `.env`, tokens) | Blocker if violated |
+
+**Passing score:** 60/100 on the autograder. Your teacher also reviews quality against the rubric (incremental config, `>` boundary, tool-choice writing, Job configuration).
+
+## Repository layout
+
+```text
+data-assignment-week-13/
+├── task-1/
+│   └── pyspark_exploration.ipynb    # or .py export from Databricks
+├── task-2/
+│   ├── dbt_project.yml              # your ported Week 10 project
+│   ├── models/                      # your dbt models
+│   ├── profiles.yml.example
+│   └── WRITEUP.md                   # incremental build write-up + DESCRIBE HISTORY
+├── task-3/                          # Git-backed Job scheduling
+│   ├── SCHEDULING.md                # Jobs vs Airflow write-up + Job Run URL
+│   └── screenshots/                 # Job config, green run, paused trigger
+├── task-4/                          # optional bonuses only (create if needed)
+├── .env.example
+├── AI_ASSIST.md                     # LLM interaction log
+└── README.md
+```
+
+## Setup
+
+```bash
+cp .env.example .env          # fill in Databricks connection values
+cd task-2
+cp profiles.yml.example profiles.yml
+export $(grep -v '^#' ../.env | xargs)   # or source manually
+dbt debug
+```
+
+Use Python 3.11 or 3.12 for dbt if `dbt debug` crashes on import (`uvx --python 3.11 --from dbt-databricks dbt debug`).
+
+## Check your score locally
+
+```bash
+bash .hyf/test.sh
+cat .hyf/score.json
+```
+
+## Scoring ladder (autograder)
+
+| Score | What the grader checks |
+| --- | --- |
+| 15 | Required files present (`task-1` notebook, `task-2/dbt_project.yml`, `WRITEUP.md`, `SCHEDULING.md`, `AI_ASSIST.md`) |
+| 25 | Task 1 notebook mentions `show` and borough/payment_type work |
+| 30 | Task 2 has incremental config (`materialized='incremental'`, `merge`, `unique_key`) and a filled `WRITEUP.md` |
+| 15 | Task 3 has screenshots in `task-3/screenshots/`, Job Run URL, and a filled `SCHEDULING.md` |
+| 15 | `AI_ASSIST.md` contains documented prompt and rationale |
+| Pass | Secrets hygiene (no committed `.env` / `profiles.yml` / `dapi` tokens) |
+
+Governance and streaming bonuses are teacher-reviewed only; they do not affect the autograder score.
+
+## Instructor / track maintainer
+
+This repo is the Week 13 student scaffold. Teacher rubric: `week_13__assignment_rubric.md` in the [datatrack](https://github.com/HackYourFuture/datatrack) curriculum repo (not shared with students).
diff --git a/docs/metrics.md b/docs/metrics.md
new file mode 100644
index 0000000..b47cc26
--- /dev/null
+++ b/docs/metrics.md
@@ -0,0 +1,20 @@
+# Model grain, metrics and quality
+
+`fct_trips` contains one row per pickup borough and pickup date. Its composite merge key is `(pickup_borough, pickup_date)`. Despite the inherited model name, it is a daily borough aggregate, not a record of individual trips.
+
+| Metric | Meaning |
+|---|---|
+| `trip_count` | Count of source records that pass staging and match a pickup zone |
+| `total_fare` | Sum of meter fares in USD; not total payments or profit |
+| `avg_tip_pct` | Mean of the implemented per-record tip/fare ratios; ratio rather than percent |
+| `avg_trip_distance` | Mean recorded distance in miles |
+
+Staging drops missing pickup IDs and negative/NULL fares. The zone join drops unmatched pickup IDs; a matching lookup row labeled `Unknown` still remains. Missing pickup timestamps are reported by a not-null test rather than filtered explicitly. There is no additional distance, tip or physical-trip deduplication policy in this lab.
+
+The inherited `safe_divide` macro casts operands to `numeric` and uses `NULLIF` for zero denominators. On Databricks, bare `NUMERIC` defaults to `DECIMAL(10,0)`, so fractional dollar values can lose precision before division. The current SQL is preserved; a future correction should set an explicit decimal scale and validate fractional-cent/zero-denominator cases. Recorded tip ratios should not be treated as a newly verified exact-money calculation. [Databricks decimal type](https://docs.databricks.com/aws/en/sql/language-manual/data-types/decimal-type).
+
+TLC's public yellow-taxi dictionary states that recorded tips exclude cash tips. The course tables are provided separately, so the public field definitions are context rather than independently verified provenance of every loaded row. [TLC yellow-taxi field dictionary](https://www.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf).
+
+The warning test returns groups whose mean tip ratio exceeds 1. The saved Job run returned 158 such groups with warning severity. A warning does not fail that run and does not prove the underlying values are correct or incorrect; it identifies groups to investigate.
+
+The PySpark notebook answers different raw-data questions. Its borough query joins raw trips to zones without the dbt fare filter; its payment query averages raw `total_amount`. These outputs must not be equated with the mart's fare sum or trip counts. When combining daily rows, sum counts/fares; do not average daily averages without the relevant contributing counts.
diff --git a/docs/provenance.md b/docs/provenance.md
new file mode 100644
index 0000000..32bce85
--- /dev/null
+++ b/docs/provenance.md
@@ -0,0 +1,26 @@
+# Provenance and retained work
+
+This repository is Mohammed Alfakih's personal fork of [HackYourAssignment/c55-data-week-13](https://github.com/HackYourAssignment/c55-data-week-13), completed during HackYourFuture's Data Track. The course supplied the scaffold, tasks and shared Databricks tables. The original assignment guide is preserved in [hyf-assignment.md](hyf-assignment.md).
+
+The portfolio update starts from [original completed snapshot `4996933`](https://github.com/mohammedalfakih-dev/c55-data-week-13/commit/49969330ab22773dc7d42323492a539c2bb5cdbc), titled “complete Databricks job scheduling evidence”. It retains the existing repository and Git history.
+
+## What is preserved
+
+- The complete PySpark notebook, code cells and saved outputs.
+- All SQL transformation expressions, incremental configuration and model names.
+- The dependency and package lockfiles, profile template and source configuration.
+- Original Databricks Job screenshots, including the warning and paused trigger.
+- Original recorded timing and Delta history values, with clearer context.
+- The earlier PostgreSQL business-answer sheet, with an added origin note. Before that note, its SHA-256 matched the Week 10 solution's answer sheet exactly.
+- Existing course support files and their history.
+
+## Portfolio documentation changes
+
+- Replace the assignment landing page with project questions, data flow, output links and setup guidance.
+- Replace task instruction placeholders with explanations of the completed work.
+- Correct green-taxi descriptions to match the configured yellow-taxi source and explain the daily mart grain.
+- Explain the strict incremental date boundary, warning test, inherited numeric-cast precision and reproduction limits.
+- Add a locked offline dbt parse check without warehouse credentials.
+- Ignore local Python environments alongside existing dbt artifacts.
+
+The planned repository name is `nyc-taxi-databricks-lakehouse`; renaming happens after PR review. Documentation uses relative project links so those links remain usable after the rename. Live Databricks resources are not changed by this PR.
diff --git a/docs/queries.sql b/docs/queries.sql
new file mode 100644
index 0000000..0472782
--- /dev/null
+++ b/docs/queries.sql
@@ -0,0 +1,35 @@
+-- Review examples for the CURRENT daily borough mart, not recorded query outputs.
+-- Replace dev_yourname with your configured DBT_SCHEMA.
+
+-- Total meter fare by pickup borough.
+SELECT pickup_borough, SUM(total_fare) AS total_meter_fare
+FROM hyf.dev_yourname.fct_trips
+GROUP BY pickup_borough
+ORDER BY total_meter_fare DESC;
+
+-- Busiest pickup date by accepted record count.
+SELECT pickup_date, SUM(trip_count) AS accepted_trip_records
+FROM hyf.dev_yourname.fct_trips
+GROUP BY pickup_date
+ORDER BY accepted_trip_records DESC
+LIMIT 1;
+
+-- The daily groups flagged by the warning-level tip-ratio test.
+SELECT pickup_borough, pickup_date, avg_tip_pct
+FROM hyf.dev_yourname.fct_trips
+WHERE avg_tip_pct > 1
+ORDER BY avg_tip_pct DESC;
+
+-- Recorded daily volume distribution; approximate median, not an exact median.
+SELECT pickup_borough,
+    percentile_approx(trip_count, 0.5) AS approximate_median_daily_records
+FROM hyf.dev_yourname.fct_trips
+WHERE pickup_borough IN ('Manhattan', 'Brooklyn')
+GROUP BY pickup_borough;
+
+-- Verify loaded date coverage and table history in the connected workspace.
+SELECT MIN(pickup_date) AS first_pickup_date, MAX(pickup_date) AS last_pickup_date,
+    COUNT(*) AS daily_borough_rows
+FROM hyf.dev_yourname.fct_trips;
+
+DESCRIBE HISTORY hyf.dev_yourname.fct_trips;
diff --git a/docs/setup.md b/docs/setup.md
new file mode 100644
index 0000000..e1abab8
--- /dev/null
+++ b/docs/setup.md
@@ -0,0 +1,78 @@
+# Setup and reproduction
+
+Python 3.12 and [uv](https://docs.astral.sh/uv/) install the dependencies recorded in `task-2/uv.lock`. From the repository root:
+
+```sh
+cd task-2
+uv sync --frozen --python 3.12
+```
+
+The lockfile is preserved. The locally checked combination is dbt-core 1.11.12 and dbt-databricks 1.12.3; this is not a recommendation to upgrade the project or proof of warehouse execution.
+
+## Offline review without Databricks
+
+Use a temporary copy of the profile template with dummy connection values. `dbt deps` downloads the declared dbt package; `dbt parse` validates Jinja/YAML and generates a manifest without connecting to a warehouse. The dummy settings deliberately cannot run a database build. [dbt parse documentation](https://docs.getdbt.com/reference/commands/parse).
+
+PowerShell, from `task-2`, in a separate terminal from any real Databricks session:
+
+```powershell
+$parseProfileDir = Join-Path $env:TEMP 'nyc-taxi-dbt-parse'
+New-Item -ItemType Directory -Force -Path $parseProfileDir | Out-Null
+Copy-Item profiles.yml.example (Join-Path $parseProfileDir 'profiles.yml')
+$env:DATABRICKS_HOST = 'offline.invalid'
+$env:DATABRICKS_HTTP_PATH = '/sql/1.0/warehouses/offline'
+$env:DATABRICKS_TOKEN = 'offline-placeholder'
+$env:DBT_SCHEMA = 'portfolio_parse'
+$env:DBT_SEND_ANONYMOUS_USAGE_STATS = 'false'
+uv run dbt deps --profiles-dir $parseProfileDir
+uv run dbt parse --profiles-dir $parseProfileDir --no-partial-parse
+```
+
+POSIX shell, from `task-2`, also in a separate terminal:
+
+```sh
+parse_profile_dir=$(mktemp -d)
+cp profiles.yml.example "$parse_profile_dir/profiles.yml"
+export DATABRICKS_HOST=offline.invalid
+export DATABRICKS_HTTP_PATH=/sql/1.0/warehouses/offline
+export DATABRICKS_TOKEN=offline-placeholder
+export DBT_SCHEMA=portfolio_parse
+export DBT_SEND_ANONYMOUS_USAGE_STATS=false
+uv run dbt deps --profiles-dir "$parse_profile_dir"
+uv run dbt parse --profiles-dir "$parse_profile_dir" --no-partial-parse
+```
+
+No real credentials are needed for this path. Dependency downloads require internet access on the first run. Generated files in `target/`, `logs/`, `dbt_packages/` and `.venv/` are ignored by Git. A successful parse is not a successful SQL build or data-test run.
+
+## Databricks warehouse run
+
+Required resources:
+
+- A SQL warehouse with connection permissions.
+- Read access to `hyf.nyc_yellow.raw_trips` and `hyf.nyc_yellow.raw_zones`.
+- Your own writable target schema in catalog `hyf`.
+
+The source tables are preloaded, normalized class tables. dbt requires `pickup_datetime`, `pickup_location_id`, `fare_amount`, `tip_amount` and `trip_distance`; zones require `location_id` and `borough`. The PySpark notebook additionally uses `payment_type` and `total_amount`. Public TLC Parquet files cannot be substituted without first loading and normalizing their field names.
+
+For a different workspace/catalog, adapt `models/staging/_sources.yml`, `profiles.yml.example` and the notebook table names consistently. There is no source-loading or catalog-provisioning script in this repository.
+
+Copy the profile template to ignored `task-2/profiles.yml`. In your real connection terminal, set `DATABRICKS_HOST`, `DATABRICKS_HTTP_PATH`, `DATABRICKS_TOKEN` and `DBT_SCHEMA` using your local connection details. `.env.example` documents these names; dbt does not automatically load the root `.env` file. The host is the hostname without an `https://` prefix; the HTTP path is your warehouse path.
+
+From `task-2`:
+
+```sh
+uv run dbt deps --profiles-dir .
+uv run dbt debug --profiles-dir .
+uv run dbt build --profiles-dir . --select +fct_trips --full-refresh
+uv run dbt build --profiles-dir . --select +fct_trips
+```
+
+The leading `+` includes upstream staging models, making the first build suitable for a fresh schema. The full refresh rebuilds the lab's target mart. The incremental rerun uses the existing strict newer-date filter. These commands are documented for reproduction; they were not run against a live warehouse for this PR.
+
+Query your own target using the examples in [queries.sql](queries.sql), replacing `dev_yourname` with `DBT_SCHEMA`. [Incremental behavior and recovery limits](../task-2/WRITEUP.md).
+
+## PySpark and Jobs
+
+The notebook needs suitable Spark compute in Databricks and source-table read access. Local dbt parsing does not execute the notebook. The [Job guide](../task-3/README.md) explains recreating the Git-backed dbt task; the saved Job did not include the notebook.
+
+After the repository rename, update any local clone's remote URL and any Databricks Job Git source to the new URL. The `task-2` directory, `main` branch and `fct_trips` model/job selector remain valid because this PR preserves those paths and names.
diff --git a/docs/validation.md b/docs/validation.md
new file mode 100644
index 0000000..d489637
--- /dev/null
+++ b/docs/validation.md
@@ -0,0 +1,21 @@
+# Validation scope
+
+Checked locally on 2 October 2026 (Europe/Amsterdam):
+
+| Check | Result |
+|---|---|
+| Existing locked environment | Installed with Python 3.12; lockfile unchanged |
+| dbt package resolution | Existing `dbt_utils` 1.4.1 installed; package lockfile unchanged |
+| Offline dbt parse | Passed with dbt-core 1.11.12 / dbt-databricks 1.12.3 and dummy connection settings |
+| Parsed project graph | 3 models and 9 data-test definitions; these tests were parsed, not executed |
+| Existing HYF static grader | Passed, 100/100; not a Databricks runtime test |
+| Notebook | JSON/code syntax checked; notebook code and saved outputs unchanged |
+| Preservation | Git content hashes matched for 15 protected original files, including notebook, mart, macro, lockfiles, source settings and screenshots |
+| SQL logic | All tracked transformation/test SQL unchanged after excluding comments |
+| Workflow configuration | YAML syntax checked; GitHub execution must be inspected on the PR |
+
+The new workflow repeats the locked install and offline parse without warehouse credentials. [dbt parse](https://docs.getdbt.com/reference/commands/parse) does not connect to a warehouse or execute SQL; it cannot prove that a cloud build or data test will succeed.
+
+No Databricks notebook, warehouse build, cloud Job or schedule was run or changed for this PR. Saved notebook results, July timing/history entries and Job screenshots are historical coursework evidence. The screenshot's warning result is retained and explained, rather than presented as a clean data-quality pass.
+
+Known incremental and numeric-precision limits are documented in [WRITEUP](../task-2/WRITEUP.md) and [metrics](metrics.md). The original implementation remains available for review and later improvements.
diff --git a/task-1/README.md b/task-1/README.md
index 569d9a9..15e2f14 100644
--- a/task-1/README.md
+++ b/task-1/README.md
@@ -1,14 +1,26 @@
-# Task 1: PySpark exploration
+# PySpark exploration
 
-Add your Databricks notebook here.
+The completed [notebook](pyspark_exploration.ipynb) reads the course's normalized yellow-taxi tables, joins pickup locations to the zone lookup and groups records by borough. It separately groups raw trips by payment code and averages `total_amount`.
 
-**Filename:** `pyspark_exploration.ipynb` (or export as `pyspark_exploration.py` from Databricks).
+Both queries display small aggregate DataFrames with `show()`; the notebook does not collect the full raw dataset into local Python memory. A concluding note explains when PySpark DataFrame processing or dbt SQL is the better fit.
 
-**Must include:**
+## Saved outputs
 
-- A join from `hyf.nyc_yellow.raw_trips` to `hyf.nyc_yellow.raw_zones` to answer which pickup borough has the most trips.
-- An aggregation of average `total_amount` per `payment_type`.
-- Results displayed with `show()` on small aggregated DataFrames (not `collect()` on the raw table).
-- Two or three sentences on when you would choose PySpark versus dbt SQL.
+The exported notebook records Manhattan as the highest-count joined pickup borough, with 112,028,489 records. Recorded average total charges:
 
-See the [Week 13 assignment](https://www.notion.so/hackyourfuture/Assignment-2af50f64ffc98112b371c42a3f469749) for full requirements.
+| Payment code | Average total charge (USD) |
+|---|---:|
+| 0 | 23.49 |
+| 1 | 30.00 |
+| 2 | 23.75 |
+| 3 | 9.03 |
+| 4 | 2.16 |
+| 5 | 14.89 |
+
+These are saved coursework outputs; data coverage and counts were not newly reproduced for this documentation update. The raw exploration population differs from the filtered dbt mart population.
+
+## Run it in Databricks
+
+Import the notebook into a Databricks workspace and attach suitable Spark compute with read access to `hyf.nyc_yellow.raw_trips` and `hyf.nyc_yellow.raw_zones`. For another workspace, adapt the two table references to equivalent normalized tables. Running it scans the available source data; it is not a bundled local dataset.
+
+The notebook and its saved cell outputs are preserved unchanged.
diff --git a/task-1/pyspark_exploration.ipynb b/task-1/pyspark_exploration.ipynb
new file mode 100644
index 0000000..a73b5d1
--- /dev/null
+++ b/task-1/pyspark_exploration.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "812be89c-94b7-4212-8456-88f19f6fd2f7",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "source": [
+    "# Task 1: PySpark Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "finishTime": 1785368532353,
+     "inputWidgets": {},
+     "nuid": "30e0fce1-105c-492a-89cb-3f7aeb888d11",
+     "showTitle": false,
+     "startTime": 1785368531489,
+     "submitTime": 1785368531211,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pyspark.sql import functions as F\n",
+    "\n",
+    "trips = spark.read.table(\"hyf.nyc_yellow.raw_trips\")\n",
+    "zones = spark.read.table(\"hyf.nyc_yellow.raw_zones\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "7ce90937-7600-4bb1-8a7d-1ea73d9a7376",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "source": [
+    "## Question 1: Which pickup borough has the most trips?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "finishTime": 1785368544983,
+     "inputWidgets": {},
+     "nuid": "f6782a41-0e18-46bd-921e-72226d46cc0f",
+     "showTitle": false,
+     "startTime": 1785368532402,
+     "submitTime": 1785368531237,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+---------+----------+\n|borough  |trip_count|\n+---------+----------+\n|Manhattan|112028489 |\n+---------+----------+\nonly showing top 1 row\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "borough_counts = (\n",
+    "    trips\n",
+    "    .join(\n",
+    "        zones,\n",
+    "        trips.pickup_location_id == zones.location_id,\n",
+    "        \"inner\"\n",
+    "    )\n",
+    "    .groupBy(\"borough\")\n",
+    "    .agg(F.count(\"*\").alias(\"trip_count\"))\n",
+    "    .orderBy(F.col(\"trip_count\").desc())\n",
+    ")\n",
+    "\n",
+    "borough_counts.show(1, truncate=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "4c51b8f0-4857-4ca4-938d-2b29790dd365",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "source": [
+    "## Question 2: What is the average total amount per payment type?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "finishTime": 1785368553297,
+     "inputWidgets": {},
+     "nuid": "be5d5e8a-4172-49c2-8508-ba14f345c7e4",
+     "showTitle": false,
+     "startTime": 1785368545006,
+     "submitTime": 1785368531260,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+------------+----------------+\n|payment_type|avg_total_amount|\n+------------+----------------+\n|0           |23.49           |\n|1           |30.0            |\n|2           |23.75           |\n|3           |9.03            |\n|4           |2.16            |\n|5           |14.89           |\n+------------+----------------+\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "payment_average = (\n",
+    "    trips\n",
+    "    .groupBy(\"payment_type\")\n",
+    "    .agg(\n",
+    "        F.round(\n",
+    "            F.avg(\"total_amount\"),\n",
+    "            2\n",
+    "        ).alias(\"avg_total_amount\")\n",
+    "    )\n",
+    "    .orderBy(\"payment_type\")\n",
+    ")\n",
+    "\n",
+    "payment_average.show(truncate=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "49f61b1d-f921-4a6f-9ba5-0b50aca0a175",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "source": [
+    "## Findings\n",
+    "\n",
+    "The pickup borough with the most trips is **Manhattan**, with **112,028,489 trips**.\n",
+    "\n",
+    "The average `total_amount` is **23.49** for payment type 0, **30.00** for type 1, **23.75** for type 2, **9.03** for type 3, **2.16** for type 4, and **14.89** for type 5.\n",
+    "\n",
+    "## PySpark versus dbt SQL\n",
+    "\n",
+    "I would use PySpark for large-scale exploration or transformations that require distributed processing, DataFrame operations, or custom Python logic. I would use dbt SQL for tested, documented, and version-controlled transformations that create reusable analytics models."
+   ]
+  }
+ ],
+ "metadata": {
+  "application/vnd.databricks.v1+notebook": {
+   "computePreferences": null,
+   "dashboards": [],
+   "environmentMetadata": {
+    "base_environment": "",
+    "environment_version": "5"
+   },
+   "inputWidgetPreferences": null,
+   "language": "python",
+   "notebookMetadata": {
+    "pythonIndentUnit": 4
+   },
+   "notebookName": "pyspark_exploration",
+   "widgets": {}
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/task-2/README.md b/task-2/README.md
index 5706176..e867334 100644
--- a/task-2/README.md
+++ b/task-2/README.md
@@ -1,12 +1,18 @@
-# Task 2: dbt on Databricks
+# dbt and Delta daily mart
 
-Copy your **Week 10 dbt project** into this folder (or start from the `week-13-databricks` branch of [nyc-taxi-dbt-reference](https://github.com/lassebenni/nyc-taxi-dbt-reference)).
+This project reads normalized yellow-taxi trip and zone tables from `hyf.nyc_yellow`, creates staging views and builds a Delta incremental mart called `fct_trips`.
 
-**Required changes:**
+Despite its inherited name, the mart has one row per **pickup borough and pickup date**. Its composite merge key matches that grain. SQL transformations and the original model name are preserved.
 
-1. Install `dbt-databricks` locally (`pip install dbt-databricks` or `uv tool install dbt-databricks`).
-2. Copy `profiles.yml.example` to `profiles.yml` (git-ignored) and export the env vars from `.env.example`.
-3. Configure `fct_trips` as `materialized='incremental'` with `incremental_strategy='merge'` and a real `unique_key`.
-4. Run `dbt build --select fct_trips --full-refresh` for the baseline, then `dbt build --select fct_trips` for the incremental rerun. Document both timings plus your explanation in `WRITEUP.md`.
+| Resource | Purpose |
+|---|---|
+| `stg_trips` | Retain required trip fields, filter missing pickup IDs and negative fares, derive tip ratio |
+| `stg_zones` | Expose location IDs and borough labels |
+| `fct_trips` | Join pickup zones and aggregate daily counts, fares, tip ratios and distances |
+| `safe_divide` | Return NULL for zero/NULL denominator using the inherited numeric casts |
 
-**Do not commit:** `profiles.yml`, `.env`, or any Databricks token.
+Checks cover staging pickup timestamps/IDs, lookup ID uniqueness and essential lookup fields, the mart's composite grain and reporting keys. A singular warning test returns daily groups where average tip/fare ratio exceeds 1.
+
+[Setup and offline parsing](../docs/setup.md) · [Incremental design and historical timings](WRITEUP.md) · [Metric definitions](../docs/metrics.md).
+
+`reports/answers.md` is retained from the earlier PostgreSQL assignment. Its source table, population and recorded values are not results of this Databricks model. Use [current-model query examples](../docs/queries.sql) when reviewing this mart.
diff --git a/task-2/WRITEUP.md b/task-2/WRITEUP.md
index f3d1eb3..eaa750f 100644
--- a/task-2/WRITEUP.md
+++ b/task-2/WRITEUP.md
@@ -1,24 +1,53 @@
-# Task 2 write-up: incremental build timings & Delta history
+# Incremental design, recorded timings and Delta history
 
-Fill in after running `dbt build --select fct_trips --full-refresh` baseline followed by `dbt build --select fct_trips` incremental rerun against Databricks.
+The `fct_trips` model is materialized as an incremental Delta table using `incremental_strategy='merge'`. Its grain and `unique_key` are `(pickup_borough, pickup_date)`. The assignment name is retained, although this is a daily borough mart rather than a trip-level fact.
 
-## First build (full / initial load with --full-refresh)
+## First build and incremental boundary
 
-- **Wall-clock time:**
-- **Notes:** (optional: warehouse size, any errors you fixed)
+During the first build or a full refresh, `is_incremental()` is false and the model aggregates the available source history. On an existing incremental target, the model applies:
 
-## Second build (incremental rerun)
+```sql
+WHERE CAST(t.pickup_datetime AS DATE) > (
+    SELECT MAX(pickup_date)
+    FROM {{ this }}
+)
+```
 
-- **Wall-clock time:**
+`{{ this }}` resolves to the model's target relation. The strict `>` boundary selects only dates after its global maximum pickup date. Incoming borough/day aggregates are then merged by the composite key. A merge strategy does not independently recover rows excluded by this filter.
 
-## Why was the second run faster?
+## Historical timing observations
 
-Write two or three sentences in your own words (see the assignment for the concepts you must name):
+The original coursework write-up recorded:
 
-`___`
+| Run | Wall-clock observation | Recorded outcome |
+|---|---:|---|
+| Full refresh | 20.404 seconds | Completed against `hyf-dbt-warehouse` |
+| Incremental rerun | 16.735 seconds | Completed using Delta merge |
 
-## Delta Table History (DESCRIBE HISTORY)
+The rerun can aggregate fewer dates, including none if no newer date exists. These two observations do not prove how many new rows were processed, isolate cache/warehouse effects or establish a repeatable performance improvement. They were not remeasured for this documentation update. The later scheduled-job screenshot has a different run duration and is separate evidence.
 
-Paste the output or summary of `DESCRIBE HISTORY hyf.dev_yourname.fct_trips` (showing `CREATE OR REPLACE TABLE` and `MERGE` operations) or reference a screenshot:
+## Recorded Delta history
 
-`___`
+Original command:
+
+```sql
+DESCRIBE HISTORY hyf.dev_mohammedalfakih.fct_trips;
+```
+
+| Version | Timestamp (UTC) | Recorded operation |
+|---|---|---|
+| 3 | 2026-07-30 00:57:53 | MERGE |
+| 1 | 2026-07-30 00:54:53 | CREATE OR REPLACE TABLE AS SELECT |
+
+These entries were copied into the original write-up; the live table/history is not queried by local parsing or CI.
+
+## Limits of the current filter
+
+- Late arrivals for an earlier date, updates to an existing date and additional records on the latest loaded day are skipped.
+- A missing borough/day below another borough's maximum date is also skipped; the watermark is global.
+- If an existing target is empty, `MAX(pickup_date)` is NULL and the predicate selects no records. A full refresh rebuilds that target.
+- Source deletions are not propagated by the append-only filter.
+
+A full refresh re-aggregates all available source rows and is the current recovery option. For a production adaptation, use an inclusive lookback window, re-aggregate all source records in that window and merge complete borough/day groups. Handle deleted or now-empty groups explicitly. The lookback design is a proposed improvement, not an implemented feature of this lab.
+
+[dbt incremental model documentation](https://docs.getdbt.com/docs/build/incremental-models).
diff --git a/task-2/dbt_project.yml b/task-2/dbt_project.yml
new file mode 100644
index 0000000..6dfd50f
--- /dev/null
+++ b/task-2/dbt_project.yml
@@ -0,0 +1,25 @@
+name: "nyc_taxi_borough_daily"
+version: "1.0.0"
+config-version: 2
+
+# This project connects to the profile of the same name in profiles.yml.
+profile: "nyc_taxi"
+
+model-paths: ["models"]
+macro-paths: ["macros"]
+test-paths: ["tests"]
+
+target-path: "target"
+clean-targets:
+  - "target"
+  - "dbt_packages"
+
+# Folder-level materialization defaults. Staging models stay as views (cheap,
+# always fresh); the mart is built as a table (queried repeatedly by the
+# dashboard). You can override per model with {{ config(materialized='...') }}.
+models:
+  nyc_taxi_borough_daily:
+    staging:
+      +materialized: view
+    marts:
+      +materialized: table
diff --git a/task-2/macros/safe_divide.sql b/task-2/macros/safe_divide.sql
new file mode 100644
index 0000000..7cd2e95
--- /dev/null
+++ b/task-2/macros/safe_divide.sql
@@ -0,0 +1,7 @@
+-- safe_divide(numerator, denominator)
+-- Returns numerator / denominator, or NULL when denominator is 0 or NULL.
+
+
+{% macro safe_divide(numerator, denominator) %}
+    ({{ numerator }}::numeric / NULLIF({{ denominator }}::numeric, 0))
+{% endmacro %}
diff --git a/task-2/models/marts/_fct_trips.yml b/task-2/models/marts/_fct_trips.yml
new file mode 100644
index 0000000..7d8b0e4
--- /dev/null
+++ b/task-2/models/marts/_fct_trips.yml
@@ -0,0 +1,39 @@
+version: 2
+
+models:
+  - name: fct_trips
+    description: >
+      Daily borough-level taxi mart with one row per pickup borough and pickup date.
+      It is built from stg_trips joined to stg_zones. Rows with missing pickup_location_id
+      or negative fare_amount are removed in staging, and trips with pickup location IDs
+      that do not exist in stg_zones are dropped by the inner join. The avg_tip_pct warning
+      test can return rows where small borough/date groups have unusually high average tip ratios.
+    data_tests:
+      - dbt_utils.unique_combination_of_columns:
+          arguments:
+            combination_of_columns:
+              - pickup_borough
+              - pickup_date
+
+    columns:
+      - name: pickup_borough
+        description: "Borough or TLC zone label where the trip was picked up, taken from the taxi zone lookup table."
+        data_tests:
+          - not_null
+
+      - name: pickup_date
+        description: "Calendar date of the trip pickup, derived from pickup_datetime. Unit: date."
+        data_tests:
+          - not_null
+
+      - name: trip_count
+        description: "Number of trips picked up in this borough on this date. Unit: trips."
+
+      - name: total_fare
+        description: "Sum of fare_amount for trips picked up in this borough on this date. Unit: US dollars."
+
+      - name: avg_tip_pct
+        description: "Average tip ratio for trips in this borough on this date, calculated as tip_amount divided by fare_amount. Unit: ratio, not multiplied by 100."
+
+      - name: avg_trip_distance
+        description: "Average recorded trip distance for trips picked up in this borough on this date. Unit: miles."
diff --git a/task-2/models/marts/fct_trips.sql b/task-2/models/marts/fct_trips.sql
new file mode 100644
index 0000000..9a925df
--- /dev/null
+++ b/task-2/models/marts/fct_trips.sql
@@ -0,0 +1,42 @@
+{{
+    config(
+        materialized='incremental',
+        incremental_strategy='merge',
+        unique_key=['pickup_borough', 'pickup_date']
+    )
+}}
+
+
+WITH trips AS (
+    SELECT *
+    FROM {{ ref('stg_trips') }}
+),
+
+zones AS (
+    SELECT *
+    FROM {{ ref('stg_zones') }}
+)
+
+SELECT
+    z.borough AS pickup_borough,
+    CAST(t.pickup_datetime AS DATE) AS pickup_date,
+    COUNT(*) AS trip_count,
+    SUM(t.fare_amount) AS total_fare,
+    AVG(t.tip_pct) AS avg_tip_pct,
+    AVG(t.trip_distance) AS avg_trip_distance
+
+FROM trips t
+
+INNER JOIN zones z
+    ON t.pickup_location_id = z.location_id
+
+{% if is_incremental() %}
+WHERE CAST(t.pickup_datetime AS DATE) > (
+    SELECT MAX(pickup_date)
+    FROM {{ this }}
+)
+{% endif %}
+
+GROUP BY
+    z.borough,
+    CAST(t.pickup_datetime AS DATE)
\ No newline at end of file
diff --git a/task-2/models/staging/_sources.yml b/task-2/models/staging/_sources.yml
new file mode 100644
index 0000000..676fd74
--- /dev/null
+++ b/task-2/models/staging/_sources.yml
@@ -0,0 +1,12 @@
+version: 2
+
+sources:
+  - name: nyc_taxi
+    description: Raw NYC yellow taxi trip records and zone lookup on Databricks.
+    database: hyf # Databricks catalog name
+    schema: nyc_yellow
+    tables:
+      - name: raw_trips
+        description: One row per yellow taxi trip, 2023-2025.
+      - name: raw_zones
+        description: NYC taxi zone lookup.
diff --git a/task-2/models/staging/_stg_trips.yml b/task-2/models/staging/_stg_trips.yml
new file mode 100644
index 0000000..58a572e
--- /dev/null
+++ b/task-2/models/staging/_stg_trips.yml
@@ -0,0 +1,22 @@
+version: 2
+
+models:
+  - name: stg_trips
+    description: "Staging model with one retained source record from the normalized yellow-taxi table hyf.nyc_yellow.raw_trips. It keeps the columns needed for the daily borough mart and filters missing pickup locations and negative or NULL fares."
+    columns:
+      - name: pickup_datetime
+        description: "Timestamp when the trip started. Used to derive the pickup date in the mart."
+        data_tests:
+          - not_null
+      - name: pickup_location_id
+        description: "TLC pickup zone identifier. Used to join trips to the zone lookup table."
+        data_tests:
+          - not_null
+      - name: fare_amount
+        description: "Base fare amount charged for the trip, in US dollars. Negative fares are removed in staging"
+      - name: tip_amount
+        description: "Tip amount paid for the trip, in US dollars"
+      - name: trip_distance
+        description: "Trip distance recorded by the taxi meter, in miles."
+      - name: tip_pct
+        description: "Tip ratio calculated as tip_amount divided by fare_amount. It is NULL when fare_amount is zero."
diff --git a/task-2/models/staging/_stg_zones.yml b/task-2/models/staging/_stg_zones.yml
new file mode 100644
index 0000000..cdd72bd
--- /dev/null
+++ b/task-2/models/staging/_stg_zones.yml
@@ -0,0 +1,16 @@
+version: 2
+
+models:
+  - name: stg_zones
+    description: "Staging model with one row per TLC taxi zone, built from nyc_taxi.raw_zones."
+    columns:
+      - name: location_id
+        description: "Unique TLC zone identifier. Used as the join key from trips to zones."
+        data_tests:
+          - not_null
+          - unique
+
+      - name: borough
+        description: "Borough or zone label for the TLC location, such as Manhattan, Brooklyn, Queens, Bronx, Staten Island, EWR, Unknown, or NaN."
+        data_tests:
+          - not_null
diff --git a/task-2/models/staging/stg_trips.sql b/task-2/models/staging/stg_trips.sql
new file mode 100644
index 0000000..547dc23
--- /dev/null
+++ b/task-2/models/staging/stg_trips.sql
@@ -0,0 +1,17 @@
+-- Staging model: one retained source record from hyf.nyc_yellow.raw_trips.
+-- Selects source fields, derives the tip ratio, and applies staging filters.
+-- Downstream: the daily borough mart fct_trips joins this to stg_zones.
+
+SELECT
+    pickup_datetime,
+    pickup_location_id,
+    fare_amount,
+    tip_amount,
+    trip_distance,
+    {{ safe_divide('tip_amount', 'fare_amount') }} AS tip_pct
+
+FROM {{ source('nyc_taxi', 'raw_trips') }}
+
+WHERE
+    pickup_location_id IS NOT NULL
+    AND fare_amount >= 0
diff --git a/task-2/models/staging/stg_zones.sql b/task-2/models/staging/stg_zones.sql
new file mode 100644
index 0000000..b378c7a
--- /dev/null
+++ b/task-2/models/staging/stg_zones.sql
@@ -0,0 +1,7 @@
+-- Staging model: one row per TLC zone (265 zones).
+-- Exposes location_id and borough for use as a lookup in the mart.
+
+SELECT
+    location_id,
+    borough
+FROM {{ source('nyc_taxi', 'raw_zones') }}
diff --git a/task-2/package-lock.yml b/task-2/package-lock.yml
new file mode 100644
index 0000000..7bf509f
--- /dev/null
+++ b/task-2/package-lock.yml
@@ -0,0 +1,5 @@
+packages:
+  - name: dbt_utils
+    package: dbt-labs/dbt_utils
+    version: 1.4.1
+sha1_hash: 8b27037b26f3f630c6661194d2470e720c49f6ee
diff --git a/task-2/packages.yml b/task-2/packages.yml
new file mode 100644
index 0000000..60c4d13
--- /dev/null
+++ b/task-2/packages.yml
@@ -0,0 +1,3 @@
+packages:
+  - package: dbt-labs/dbt_utils
+    version: 1.4.1
diff --git a/task-2/pyproject.toml b/task-2/pyproject.toml
new file mode 100644
index 0000000..d950451
--- /dev/null
+++ b/task-2/pyproject.toml
@@ -0,0 +1,9 @@
+[project]
+name = "c55-data-week-13"
+version = "0.1.0"
+description = "Week 13 Databricks dbt project for the NYC yellow taxi mart"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "dbt-databricks>=1.10.0",
+]
diff --git a/task-2/reports/answers.md b/task-2/reports/answers.md
new file mode 100644
index 0000000..90ceb86
--- /dev/null
+++ b/task-2/reports/answers.md
@@ -0,0 +1,101 @@
+# Business Question Answers
+
+> Historical Week 10 PostgreSQL answers, retained unchanged below. Before this explanatory note, this file was byte-for-byte identical to the Week 10 answer sheet. Its `fct_daily_borough_stats` table and recorded values are not outputs from the Week 13 Databricks yellow-taxi model. See [current-model query examples](../../docs/queries.sql); no new query results are claimed here.
+
+Queries run against `dev_mohammedalfakih.fct_daily_borough_stats`.
+
+## Q1: Highest total `total_fare` across the whole loaded dataset
+
+**SQL:**
+
+```sql
+SELECT
+    pickup_borough,
+    SUM(total_fare) AS total_fare_all_days
+FROM dev_mohammedalfakih.fct_daily_borough_stats
+GROUP BY pickup_borough
+ORDER BY total_fare_all_days DESC
+LIMIT 1;
+```
+
+**Result:** | pickup_borough | total_fare_all_days |
+| Manhattan | 493,955.62 |
+
+**Interpretation:** Manhattan generated the highest total fare across the loaded dataset, with $493,955.62 in total fare revenue.
+
+---
+
+## Q2: Day with the highest overall `trip_count`
+
+**SQL:**
+
+```sql
+SELECT
+    pickup_date,
+    SUM(trip_count) AS overall_trip_count
+FROM dev_mohammedalfakih.fct_daily_borough_stats
+GROUP BY pickup_date
+ORDER BY overall_trip_count DESC
+LIMIT 1;
+```
+
+**Result:** | pickup_date | overall_trip_count |
+| 2024-01-17 | 2,221 |
+
+**Interpretation:** January 17, 2024 had the highest total trip volume across all pickup boroughs, with 2,221 trips.
+
+---
+
+## Q3: Highest `avg_tip_pct` for any (borough, day) combination
+
+**SQL:**
+
+```sql
+SELECT
+    pickup_borough,
+    pickup_date,
+    avg_tip_pct
+FROM dev_mohammedalfakih.fct_daily_borough_stats
+ORDER BY avg_tip_pct DESC
+LIMIT 5;
+```
+
+**Result:** | pickup_borough | pickup_date | avg_tip_pct |
+
+            | Unknown        | 2024-01-30  |     2.500125 |
+            | Unknown        | 2024-01-07  | 1.3396296296 |
+            | Unknown        | 2024-01-11  |  1.104495614 |
+            | Unknown        | 2024-01-16  |            1 |
+            | Unknown        | 2024-01-18  | 0.6111111111 |
+
+**Interpretation:** The highest average tip percentage was for the Unknown borough on 2024-01-30, where the average tip was more than twice the fare; this matches the warning test and is likely caused by unusual trips in a small Unknown borough group rather than a model failure.
+
+The warning-level singular test returned these rows where avg_tip_pct > 1:
+
+| pickup_borough | pickup_date |  avg_tip_pct |
+| -------------- | ----------- | -----------: |
+| Unknown        | 2024-01-30  |     2.500125 |
+| Unknown        | 2024-01-07  | 1.3396296296 |
+| Unknown        | 2024-01-11  |  1.104495614 |
+
+## These are data quality warnings, not build errors.
+
+## Q4: Median daily `trip_count` for Manhattan vs Brooklyn
+
+**SQL:**
+
+```sql
+SELECT
+    pickup_borough,
+    percentile_cont(0.5) WITHIN GROUP (ORDER BY trip_count) AS median_daily_trip_count
+FROM dev_mohammedalfakih.fct_daily_borough_stats
+WHERE pickup_borough IN ('Manhattan', 'Brooklyn')
+GROUP BY pickup_borough
+ORDER BY pickup_borough;
+```
+
+**Result:** | pickup_borough | median_daily_trip_count |
+| Brooklyn | 248 |
+| Manhattan | 1,169.5 |
+
+**Interpretation:** Manhattan’s median daily trip count was much higher than Brooklyn’s, at about 4.7 times as many trips per day.
diff --git a/task-2/tests/assert_avg_tip_pct_within_bounds.sql b/task-2/tests/assert_avg_tip_pct_within_bounds.sql
new file mode 100644
index 0000000..212c0a8
--- /dev/null
+++ b/task-2/tests/assert_avg_tip_pct_within_bounds.sql
@@ -0,0 +1,14 @@
+{{ config(severity='warn') }}
+
+-- Business-rule warning test:
+-- returns borough/date rows where the average tip ratio is greater than 1.
+-- This means the average tip exceeded the fare amount, which is unusual.
+
+SELECT
+    pickup_borough,
+    pickup_date,
+    avg_tip_pct
+
+FROM {{ ref('fct_trips') }}
+
+WHERE avg_tip_pct > 1
\ No newline at end of file
diff --git a/task-2/uv.lock b/task-2/uv.lock
new file mode 100644
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diff --git a/task-3/README.md b/task-3/README.md
index 9aab6ad..0c8885b 100644
--- a/task-3/README.md
+++ b/task-3/README.md
@@ -1,20 +1,15 @@
-# Task 3: Schedule a Git-backed dbt Job
+# Git-backed Databricks Job
 
-Build on Chapter 5 ([Scheduling dbt Jobs](https://github.com/HackYourFuture/datatrack/blob/main/Data%20Track/Week%2013/week_13__5_scheduling_dbt_jobs.md)) by scheduling your ported dbt project directly from your GitHub repository fork.
+The saved coursework configuration runs dbt from this repository's `main` branch and project directory `task-2`, using `hyf-dbt-warehouse`. [Scheduling details and screenshots](SCHEDULING.md) record a successful manual run and a paused daily trigger.
 
-## Instructions
+To recreate it in your own Databricks workspace:
 
-1. Push your completed `task-2/` dbt project to your GitHub fork of `data-assignment-week-13` on branch `main`.
-2. In Databricks **Workflows** → **Jobs**, create a Job named `dev_yourname_fct_trips`.
-3. Add a `dbt` task type selecting **Git provider** as the source:
-   - **Git repository URL:** `https://github.com/<your_username>/data-assignment-week-13.git`
-   - **Git provider:** `GitHub`
-   - **Git reference:** `main`
-   - **Path / Project directory:** `task-2`
-4. Configure task execution:
-   - **dbt commands:** `dbt deps` followed by `dbt build --select fct_trips`
-   - **SQL warehouse:** `hyf-dbt-warehouse`
-   - **Catalog / Schema:** `hyf` / `dev_yourname`
-5. Click **Run now** and verify that the run completes with a green checkmark.
-6. Add a schedule (for example, daily at 06:00 UTC) and immediately **pause** the trigger.
-7. Fill in `SCHEDULING.md` with your answers and place your screenshots into `task-3/screenshots/`.
+1. Make the source tables and target-schema permissions available; follow [setup](../docs/setup.md).
+2. Create a Job with a dbt task, a Git provider source pointing to your fork, the desired Git reference and project directory `task-2`.
+3. Choose your SQL warehouse and configure your target catalog/schema.
+4. Use `dbt deps`, then `dbt build --select +fct_trips` so a fresh schema also builds the staging views.
+5. Run manually and inspect model/test outcomes before enabling a schedule.
+
+The historical job selected `fct_trips` without `+`; its upstream staging views had already been created. The notebook is a separate exploration and is not a task in that saved dbt Job.
+
+After renaming the GitHub repository, update the Job's Git source to the new URL before running it again. No live Job or schedule is changed by this PR. [Databricks dbt task documentation](https://docs.databricks.com/aws/en/jobs/tasks/dbt).
diff --git a/task-3/SCHEDULING.md b/task-3/SCHEDULING.md
index 2780686..1076e2b 100644
--- a/task-3/SCHEDULING.md
+++ b/task-3/SCHEDULING.md
@@ -1,23 +1,29 @@
-# Task 3 Write-up: Git-backed Job Scheduling
+# Git-backed Job scheduling evidence
 
-## Databricks Job Run URL
+The coursework Job `dev_mohammedalfakih_fct_trips` ran a dbt task from the personal GitHub fork's `main` branch, project directory `task-2`, using `hyf-dbt-warehouse`. Its commands were `dbt deps` followed by `dbt build --select fct_trips`.
 
-Paste the URL of your successful Job run from the Databricks UI address bar:
+## Historical successful run
 
-`___`
+The saved screenshot records a **manual run on 30 July 2026**, with Job Run ID `210092009327350`. Its output ends with `PASS=4 WARN=1 ERROR=0`: one incremental model and three tests passed; the tip-ratio test returned 158 warning rows. A successful Job therefore does not imply that every data-quality check was clean.
 
-## Screenshots
+[Recorded run permalink](https://adb-7405619530719547.7.azuredatabricks.net/jobs/1042517554723103/runs/210092009327350?o=7405619530719547) requires access to the original class workspace. It is not a public portfolio demo; screenshots are available below.
 
-Ensure the following screenshot files exist in `task-3/screenshots/`:
+![Historical successful manual run](screenshots/job_run_success.png)
 
-1. `job_config.png` — Showing the dbt task configuration with Git repository URL, branch `main`, path `task-2`, and warehouse `hyf-dbt-warehouse`.
-2. `job_run_success.png` — Showing a successful run log with a green checkmark and stdout execution output.
-3. `job_schedule_paused.png` — Showing the scheduled trigger set to **Paused**.
+## Configuration and Git source
 
-## Orchestration Comparison
+![Historical dbt task configuration](screenshots/job_config.png)
 
-### When would you choose Databricks Jobs versus Apache Airflow for pipeline orchestration?
+![Historical Git repository source configuration](screenshots/job_git_source.png)
 
-Write two to three sentences comparing Databricks Jobs and Apache Airflow in your own words:
+The original screenshot uses the repository's assignment name. After the planned rename, future runs should use the new Git URL. For a fresh target schema, build upstream models too with `dbt build --select +fct_trips`.
 
-`___`
+## Paused trigger
+
+The saved schedule is daily at **06:00 UTC**, with the trigger set to **Paused**. This describes the screenshot, not a newly verified live schedule state.
+
+![Historical paused daily trigger](screenshots/job_schedule_paused.png)
+
+## Databricks Jobs versus Airflow
+
+I would use Databricks Jobs when a pipeline mainly runs dbt, SQL or notebooks in Databricks because scheduling and monitoring stay in the same platform. I would use Apache Airflow when a workflow coordinates multiple systems or needs more complex dependencies across tools.
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