refactor: restructure monorepo for clean portfolio layout

- Move timesfm-forecast into apps/ directory
- Flatten Udacity portfolio projects from deep URL-encoded paths
  into data-engineering/01-XX numbered directories
- Remove old My-Data-Engineering-Portifolio/ parent directory
- Rewrite root README.md: professional overview with badges,
  project table, and repo structure diagram
- Create data-engineering/README.md with per-project descriptions
- Add README.md for 02-cassandra-modeling (was missing)
- Add README.md for 05-airflow-pipelines (was missing)
- Normalize capstone readme.md -> README.md
- Update .gitignore: add *.cfg, *.env, *.zip, *.sas7bdat,
  Jupyter checkpoints, IDE dirs; remove uv.lock exclusion
- Add dwh.cfg.example and dl.cfg.example credential templates
- Untrack real credential files (dwh.cfg, dl.cfg)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

data-engineering/05-airflow-pipelines/README.md

@@ -0,0 +1,83 @@
+# Automate Pipelines with Apache Airflow
+
+**Udacity Data Engineering Nanodegree — Project 5**
+
+## Overview
+
+Build a production-grade, orchestrated ETL pipeline using **Apache Airflow**. The pipeline runs on an hourly schedule, loading data from AWS S3 into AWS Redshift staging tables, transforming it into a star schema, and validating data quality — all using modular, reusable custom operators.
+
+## Architecture
+
+```
+S3 (raw JSON)
+    │
+    ▼
+[StageToRedshiftOperator]    ← Stage events & songs
+    │
+    ▼
+[LoadFactOperator]           ← Load songplays fact table
+    │
+    ▼
+[LoadDimensionOperator] ×4   ← Load users, songs, artists, time
+    │
+    ▼
+[DataQualityOperator]        ← Assert tables are non-empty
+```
+
+## DAG
+
+| Property | Value |
+|---|---|
+| **Schedule** | Hourly (`0 * * * *`) |
+| **Start date** | 2019-01-12 |
+| **Catchup** | Disabled |
+
+## Custom Operators
+
+| Operator | File | Purpose |
+|---|---|---|
+| `StageToRedshiftOperator` | `plugins/operators/stage_redshift.py` | COPY JSON from S3 to Redshift staging tables |
+| `LoadFactOperator` | `plugins/operators/load_fact.py` | INSERT into fact table from staging |
+| `LoadDimensionOperator` | `plugins/operators/load_dimension.py` | INSERT into dimension tables (supports truncate-insert or append) |
+| `DataQualityOperator` | `plugins/operators/data_quality.py` | Assert row counts > 0 for all tables |
+
+## Project Structure
+
+```
+05-airflow-pipelines/
+├── create_tables.sql          # DDL for Redshift tables
+├── dags/
+│   └── udac_example_dag.py    # Main DAG definition
+└── plugins/
+    ├── helpers/
+    │   └── sql_queries.py     # Shared SQL INSERT statements
+    └── operators/
+        ├── stage_redshift.py
+        ├── load_fact.py
+        ├── load_dimension.py
+        └── data_quality.py
+```
+
+## Key Concepts
+
+- **DAG design** — directed acyclic graphs for workflow orchestration
+- **Custom operators** — reusable, parameterized Airflow tasks
+- **Modular SQL helpers** — shared query library via `SqlQueries` class
+- **Data quality checks** — automated validation at end of every run
+- **AWS integration** — S3 `COPY` + Redshift connections via Airflow Connections
+
+## How to Run
+
+1. Set up an Airflow environment with AWS connections configured:
+   - `aws_credentials` — IAM access key & secret
+   - `redshift` — Redshift cluster connection string
+
+2. Copy DAG and plugins into your Airflow home:
+   ```bash
+   cp -r dags/ $AIRFLOW_HOME/dags/
+   cp -r plugins/ $AIRFLOW_HOME/plugins/
+   ```
+
+3. Run `create_tables.sql` against your Redshift cluster to create staging and DW tables.
+
+4. Enable the DAG in the Airflow UI — it will trigger hourly.