6796398924
- 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>
84 lines
2.7 KiB
Markdown
84 lines
2.7 KiB
Markdown
# Automate Pipelines with Apache Airflow
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**Udacity Data Engineering Nanodegree — Project 5**
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## Overview
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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.
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## Architecture
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```
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S3 (raw JSON)
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│
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▼
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[StageToRedshiftOperator] ← Stage events & songs
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│
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▼
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[LoadFactOperator] ← Load songplays fact table
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│
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▼
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[LoadDimensionOperator] ×4 ← Load users, songs, artists, time
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│
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▼
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[DataQualityOperator] ← Assert tables are non-empty
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```
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## DAG
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| Property | Value |
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|---|---|
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| **Schedule** | Hourly (`0 * * * *`) |
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| **Start date** | 2019-01-12 |
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| **Catchup** | Disabled |
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## Custom Operators
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| Operator | File | Purpose |
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|---|---|---|
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| `StageToRedshiftOperator` | `plugins/operators/stage_redshift.py` | COPY JSON from S3 to Redshift staging tables |
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| `LoadFactOperator` | `plugins/operators/load_fact.py` | INSERT into fact table from staging |
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| `LoadDimensionOperator` | `plugins/operators/load_dimension.py` | INSERT into dimension tables (supports truncate-insert or append) |
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| `DataQualityOperator` | `plugins/operators/data_quality.py` | Assert row counts > 0 for all tables |
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## Project Structure
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```
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05-airflow-pipelines/
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├── create_tables.sql # DDL for Redshift tables
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├── dags/
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│ └── udac_example_dag.py # Main DAG definition
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└── plugins/
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├── helpers/
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│ └── sql_queries.py # Shared SQL INSERT statements
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└── operators/
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├── stage_redshift.py
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├── load_fact.py
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├── load_dimension.py
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└── data_quality.py
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```
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## Key Concepts
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- **DAG design** — directed acyclic graphs for workflow orchestration
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- **Custom operators** — reusable, parameterized Airflow tasks
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- **Modular SQL helpers** — shared query library via `SqlQueries` class
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- **Data quality checks** — automated validation at end of every run
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- **AWS integration** — S3 `COPY` + Redshift connections via Airflow Connections
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## How to Run
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1. Set up an Airflow environment with AWS connections configured:
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- `aws_credentials` — IAM access key & secret
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- `redshift` — Redshift cluster connection string
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2. Copy DAG and plugins into your Airflow home:
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```bash
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cp -r dags/ $AIRFLOW_HOME/dags/
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cp -r plugins/ $AIRFLOW_HOME/plugins/
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```
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3. Run `create_tables.sql` against your Redshift cluster to create staging and DW tables.
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4. Enable the DAG in the Airflow UI — it will trigger hourly.
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