Mohammed A

.python-version

@@ -0,0 +1 @@
+3.12

AI_ASSIST.md

@@ -4,31 +4,19 @@ Document one place you used an LLM during this assignment.
 
 ## The problem
 
-<!-- TODO: describe the specific problem you asked an LLM about.
-     Example: "My safe_divide macro compiled but returned NULL for all rows even
-     when tip_amount and fare_amount were both non-zero." -->
-
-TODO
+When I tried to install `dbt_utils` with `dbt deps`, dbt failed with a Windows `WinError 32` file-lock error. The error said that the `dbt_packages/dbt_utils` folder was being used by another process, so dbt could not delete or replace it.
 
 ## The prompt
 
-<!-- TODO: paste the exact prompt you sent to the LLM. -->
-
-TODO
+I am working on a dbt assignment on Windows. When I run `uv run dbt deps`, I get this error: `[WinError 32] The process cannot access the file because it is being used by another process: 'dbt_packages\\dbt_utils'`. I am using uv and dbt_utils version 1.4.1. How can I fix this without breaking my project?
 
 ## The response
 
-<!-- TODO: summarise or paste what the LLM returned. -->
-
-TODO
+The LLM explained that this was not a dbt code problem, but a Windows file-lock problem. It suggested closing anything that might be using the `dbt_packages` folder, such as VS Code tabs, File Explorer, or a running `dbt docs serve` process. It also suggested deleting `dbt_packages` and `package-lock.yml`, then running `uv run dbt deps` again from PowerShell.
 
 ## Reflection
 
-<!-- TODO: what did you change, keep, or discard after reviewing the LLM's answer?
-     Be specific: "I kept the NULLIF suggestion but changed the column alias from
-     'ratio' to 'tip_pct' to match the assignment schema." -->
-
-TODO
+I kept the advice to close possible locking processes and rerun the command from PowerShell. After removing the locked package folder and running `uv run dbt deps` again, dbt installed `dbt_utils` successfully. I did not change my models because the problem was related to the local Windows file system, not my SQL or dbt project logic.
 
 ---
 

dbt_project.yml

@@ -1,9 +1,9 @@
-name: 'nyc_taxi_borough_daily'
-version: '1.0.0'
+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_borough_daily'
+profile: "nyc_taxi_borough_daily"
 
 model-paths: ["models"]
 macro-paths: ["macros"]

macros/safe_divide.sql

@@ -1,10 +1,7 @@
 -- safe_divide(numerator, denominator)
 -- Returns numerator / denominator, or NULL when denominator is 0 or NULL.
--- Use for tip_pct = tip_amount / fare_amount and similar ratio columns.
+
 
 {% macro safe_divide(numerator, denominator) %}
-    -- TODO: implement the macro body.
-    -- Use NULLIF(denominator, 0) to avoid division-by-zero errors.
-    -- Return only the SQL expression (no SELECT, no semicolon).
-    NULL
+    ({{ numerator }}::numeric / NULLIF({{ denominator }}::numeric, 0))
 {% endmacro %}

models/marts/_fct_daily_borough_stats.yml

@@ -3,22 +3,36 @@ version: 2
 models:
   - name: fct_daily_borough_stats
     description: >
-      TODO: state the grain (one row per ___), the source lineage
-      (built from ___ and ___), and at least one known caveat
-      (e.g. rows dropped in staging, any WARN-severity tests).
-    # TODO: Task 5 -- add the compound uniqueness test on the mart's primary
-    # key (pickup_borough, pickup_date). You need the dbt_utils package for
-    # this: declare it in packages.yml and run `dbt deps` first.
+      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:
+          combination_of_columns:
+            - pickup_borough
+            - pickup_date
+
     columns:
       - name: pickup_borough
-        description: "TODO: explain what this column contains and where it comes from"
+        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: "TODO: explain units and how it is derived"
+        description: "Calendar date of the trip pickup, derived from pickup_datetime. Unit: date."
+        data_tests:
+          - not_null
+
       - name: trip_count
-        description: "TODO: explain what is counted (unit: number of trips)"
+        description: "Number of trips picked up in this borough on this date. Unit: trips."
+
       - name: total_fare
-        description: "TODO: explain units (USD) and what fare_amount represents"
+        description: "Sum of fare_amount for trips picked up in this borough on this date. Unit: US dollars."
+
       - name: avg_tip_pct
-        description: "TODO: explain the ratio (tip_amount / fare_amount)"
+        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: "TODO: explain units (miles, from TLC source data)"
+        description: "Average recorded trip distance for trips picked up in this borough on this date. Unit: miles."

models/marts/fct_daily_borough_stats.sql

@@ -14,25 +14,18 @@ zones AS (
 )
 
 SELECT
-    -- TODO: join trips to zones on pickup_location_id = location_id.
-    --   Use an INNER JOIN: a few trips have a pickup_location_id with no matching
-    --   zone (e.g. 999). INNER JOIN drops those so pickup_borough is never NULL and
-    --   can serve as part of the mart's primary key (your not_null test needs this).
-    -- TODO: aggregate to grain (pickup_borough, pickup_date)
-    -- Required output columns:
-    --   pickup_borough   TEXT     - z.borough
-    --   pickup_date      DATE     - pickup_datetime::date
-    --   trip_count       BIGINT   - count(*)
-    --   total_fare       NUMERIC  - sum(fare_amount)
-    --   avg_tip_pct      NUMERIC  - avg(tip_pct)
-    --   avg_trip_distance NUMERIC - avg(trip_distance)
-    NULL AS pickup_borough,
-    NULL AS pickup_date,
-    NULL AS trip_count,
-    NULL AS total_fare,
-    NULL AS avg_tip_pct,
-    NULL AS avg_trip_distance
+    z.borough AS pickup_borough,
+    t.pickup_datetime::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
--- TODO: add JOIN to zones here
--- TODO: add GROUP BY here
+
+INNER JOIN zones z
+    ON t.pickup_location_id = z.location_id
+
+GROUP BY
+    z.borough,
+    t.pickup_datetime::date

models/staging/_sources.yml

@@ -2,9 +2,9 @@ version: 2
 
 sources:
   - name: nyc_taxi
-    schema: nyc_taxi     # TODO: confirm this matches the schema where raw_trips and raw_zones live
+    schema: nyc_taxi # TODO: confirm this matches the schema where raw_trips and raw_zones live
     tables:
       - name: raw_trips
-        description: "TODO: one sentence on what this table contains and its grain"
+        description: "Raw NYC Green Taxi trip records. Each row represents one taxi trip from the loaded January 2024 dataset."
       - name: raw_zones
-        description: "TODO: one sentence on what this table contains"
+        description: "Raw TLC taxi zone lookup table. Each row represents one taxi location zone and its borough."

models/staging/_stg_trips.yml

@@ -2,19 +2,21 @@ version: 2
 
 models:
   - name: stg_trips
-    description: "TODO: state the grain (one row per ___) and what source this reads from"
+    description: "Staging model with one row per NYC Green Taxi trip, built from nyc_taxi.raw_trips. It keeps the columns needed for the borough daily mart and filters rows with missing pickup location or negative fare."
     columns:
       - name: pickup_datetime
-        description: "TODO"
-        # TODO: Task 5 -- add not_null tests on every column used as a join or
-        # group-by key. See the chapter's dbt Tests section for the syntax.
+        description: "Timestamp when the trip started. Used to derive the pickup date in the mart."
+        data_tests:
+          - not_null
       - name: pickup_location_id
-        description: "TODO"
+        description: "TLC pickup zone identifier. Used to join trips to the zone lookup table."
+        data_tests:
+          - not_null
       - name: fare_amount
-        description: "TODO"
+        description: "Base fare amount charged for the trip, in US dollars. Negative fares are removed in staging"
       - name: tip_amount
-        description: "TODO"
+        description: "Tip amount paid for the trip, in US dollars"
       - name: trip_distance
-        description: "TODO"
+        description: "Trip distance recorded by the taxi meter, in miles."
       - name: tip_pct
-        description: "TODO"
+        description: "Tip ratio calculated as tip_amount divided by fare_amount. It is NULL when fare_amount is zero."

models/staging/_stg_zones.yml

@@ -2,11 +2,15 @@ version: 2
 
 models:
   - name: stg_zones
-    description: "TODO: state the grain and what source this reads from"
+    description: "Staging model with one row per TLC taxi zone, built from nyc_taxi.raw_zones."
     columns:
       - name: location_id
-        description: "TODO"
-        # TODO: Task 5 -- this column is the join key. Which two generic tests
-        # guarantee a clean one-to-many join from stg_trips?
+        description: "Unique TLC zone identifier. Used as the join key from trips to zones."
+        data_tests:
+          - not_null
+          - unique
+
       - name: borough
-        description: "TODO"
+        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

models/staging/stg_trips.sql

@@ -3,11 +3,15 @@
 -- Downstream: fct_daily_borough_stats joins this to stg_zones.
 
 SELECT
-    -- TODO: select the columns you need for the mart:
-    --   pickup_datetime, pickup_location_id, fare_amount, tip_amount, trip_distance
-    --
-    -- TODO: add tip_pct using {{ safe_divide('tip_amount', 'fare_amount') }}
-    --
-    -- TODO: filter out rows where pickup_location_id IS NULL or fare_amount < 0
+    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

models/staging/stg_zones.sql

@@ -2,6 +2,6 @@
 -- Exposes location_id and borough for use as a lookup in the mart.
 
 SELECT
-    -- TODO: select location_id and borough from {{ source('nyc_taxi', 'raw_zones') }}
-
+    location_id,
+    borough
 FROM {{ source('nyc_taxi', 'raw_zones') }}

package-lock.yml

@@ -0,0 +1,5 @@
+packages:
+  - name: dbt_utils
+    package: dbt-labs/dbt_utils
+    version: 1.4.1
+sha1_hash: 8b27037b26f3f630c6661194d2470e720c49f6ee

packages.yml

@@ -1,5 +1,3 @@
-# TODO: Task 5 -- declare the dbt-labs/dbt_utils package here, then run
-# `dbt deps` to install it. You need it for the compound uniqueness test
-# on the mart. See https://hub.getdbt.com/dbt-labs/dbt_utils/latest/
-# for the package block syntax.
-packages: []
+packages:
+  - package: dbt-labs/dbt_utils
+    version: 1.4.1

pyproject.toml

@@ -0,0 +1,10 @@
+[project]
+name = "c55-data-week-10"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "dbt-core>=1.11.12",
+    "dbt-postgres>=1.10.2",
+]

reports/answers.md

@@ -1,18 +1,25 @@
 # Business Question Answers
 
-Queries run against `dev_<your_name>.fct_daily_borough_stats`.
+Queries run against `dev_mohammedalfakih.fct_daily_borough_stats`.
 
 ## Q1: Highest total `total_fare` across the whole loaded dataset
 
 **SQL:**
 
 ```sql
--- TODO: query fct_daily_borough_stats grouped by pickup_borough, sum total_fare, order DESC
+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:** TODO
+**Result:** | pickup_borough | total_fare_all_days |
+| Manhattan | 493,955.62 |
 
-**Interpretation:** TODO (one sentence)
+**Interpretation:** Manhattan generated the highest total fare across the loaded dataset, with $493,955.62 in total fare revenue.
 
 ---
 
@@ -21,12 +28,19 @@ Queries run against `dev_<your_name>.fct_daily_borough_stats`.
 **SQL:**
 
 ```sql
--- TODO: query fct_daily_borough_stats grouped by pickup_date, sum trip_count, order DESC LIMIT 1
+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:** TODO
+**Result:** | pickup_date | overall_trip_count |
+| 2024-01-17 | 2,221 |
 
-**Interpretation:** TODO (one sentence)
+**Interpretation:** January 17, 2024 had the highest total trip volume across all pickup boroughs, with 2,221 trips.
 
 ---
 
@@ -35,23 +49,51 @@ Queries run against `dev_<your_name>.fct_daily_borough_stats`.
 **SQL:**
 
 ```sql
--- TODO: query fct_daily_borough_stats order by avg_tip_pct DESC LIMIT 5
+SELECT
+    pickup_borough,
+    pickup_date,
+    avg_tip_pct
+FROM dev_mohammedalfakih.fct_daily_borough_stats
+ORDER BY avg_tip_pct DESC
+LIMIT 5;
 ```
 
-**Result:** TODO
+**Result:** | pickup_borough | pickup_date | avg_tip_pct |
 
-**Interpretation:** TODO — note whether any avg_tip_pct > 1 rows appear and what causes them
+            | 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
--- TODO: use percentile_cont(0.5) WITHIN GROUP (ORDER BY trip_count) filtered by borough
+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:** TODO
+**Result:** | pickup_borough | median_daily_trip_count |
+| Brooklyn | 248 |
+| Manhattan | 1,169.5 |
 
-**Interpretation:** TODO (one sentence on the ratio)
+**Interpretation:** Manhattan’s median daily trip count was much higher than Brooklyn’s, at about 4.7 times as many trips per day.