This project implements a Python data pipeline that can run independently through main.py or be orchestrated with Apache Airflow. The pipeline extracts data from a public REST API, persists raw records as JSON, validates and transforms the data, saves the processed dataset as CSV, loads it into SQLite, and validates the final result. The project also demonstrates artifact-based communication between Airflow tasks, metadata-only XComs, automated testing, failure handling, idempotent loading, logging, containerization, and reproducible environment management.

• Python 3.13 — core pipeline development.
• Pandas — data transformation and tabular processing.
• Requests — data extraction from the REST API.
• SQLite — local storage for the processed dataset.

• Pytest — automated unit and failure-scenario testing.

• Python Dotenv — environment-variable management.
• Logging — pipeline execution logs and error tracking.

• Conda — isolated and reproducible Python environment management.

• Docker — containerized execution environment.
• Docker Compose — management of the Airflow services.

• Apache Airflow 3.3.0 — pipeline orchestration and task dependency management.
• LocalExecutor — local parallel task execution.
• PostgreSQL — Airflow metadata database.

• Git — source-code version control.
• GitHub — remote repository and project documentation.

The pipeline follows an artifact-based architecture. Complete datasets are persisted as JSON, CSV, and SQLite artifacts, while each processing stage validates or transforms the data before passing a reference to the next stage.

flowchart TD

    A["JSONPlaceholder REST API"]
    B["Extract users"]
    C["Persist raw data"]
    D["Raw JSON artifact"]
    E["Validate raw users"]
    F["Transform users"]
    G["Persist processed data"]
    H["Processed CSV artifact"]
    I["Create SQLite table"]
    J["Load users using full refresh"]
    K[("SQLite users table")]
    L["Validate final record count"]
    M["Execution logs"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> I
    I --> J
    J --> K
    K --> L

    B -.-> M
    E -.-> M
    F -.-> M
    J -.-> M
    L -.-> M

    classDef external fill:#F3F4F6,stroke:#4B5563,color:#111827;
    classDef process fill:#E8EEF7,stroke:#4A6280,color:#111827;
    classDef artifact fill:#E8F5E9,stroke:#2E7D32,color:#111827;
    classDef validation fill:#FFF8E1,stroke:#F9A825,color:#111827;
    classDef observability fill:#F3E8FF,stroke:#7E22CE,color:#111827;

    class A external;
    class B,C,F,G,I,J process;
    class D,H,K artifact;
    class E,L validation;
    class M observability;

Complete datasets are persisted outside Airflow's metadata database:

• Raw records are stored as JSON.
• Transformed records are stored as CSV.
• Final records are stored in SQLite.

This prevents XCom from being used as dataset storage.

Airflow tasks exchange only small metadata contracts, including:

• Artifact paths.
• Record counts.
• File sizes.
• Table names.
• Load mode.
• Validation results.

Complete user lists and pandas DataFrames are not transported through XCom.

Extraction, validation, transformation, persistence, and database logic remain inside the modules under src/.

Airflow is responsible only for:

• Task orchestration.
• Dependency management.
• Execution state.
• Scheduling and operational control.

This allows the same pipeline logic to run independently through main.py.

The pipeline supports two execution modes:

• Standalone Python execution using main.py.
• Airflow orchestration using the TaskFlow API.

This separation makes it possible to validate the pipeline independently of Airflow.

The SQLite load uses a full-refresh strategy.

Repeated executions replace the target dataset instead of appending duplicate records:

First execution  → 10 records
Second execution → 10 records

The pipeline validates data at important boundaries:

• After API extraction.
• After raw artifact persistence.
• After processed artifact persistence.
• After the SQLite load.

Invalid, empty, corrupted, or inconsistent data causes the pipeline to fail with a contextual error message.

The same pipeline logic can be executed in two ways:

• Standalone execution: main.py runs the complete pipeline directly with Python.
• Orchestrated execution: Apache Airflow coordinates the pipeline through a six-task DAG.

In the Airflow implementation, extraction and raw persistence are grouped into one task. Transformation and processed-data persistence are also grouped into one task.

extract_and_save_raw_task
→ validate_raw_users_task
→ transform_and_save_processed_task
→ create_users_table_task
→ load_users_to_database_task
→ validate_database_load_task

The business, transformation, validation, and loading logic remains inside the modules under src. Airflow is responsible for orchestration, task dependencies, execution state, and operational control.

The pipeline separates the complete datasets from the metadata used to coordinate the workflow.

The data plane contains the actual pipeline data:

• Raw user records stored in JSON.
• Transformed user records stored in CSV.
• Final user records stored in SQLite.

These artifacts persist the complete datasets outside Airflow's metadata database.

The control plane contains the information required to coordinate and validate the workflow:

• Artifact paths.
• Record counts.
• File sizes.
• Table names.
• Load mode.
• Validation results.
• Task execution status.

Airflow XComs transport only these small metadata contracts. Complete user lists and pandas DataFrames are not transported between tasks.

flowchart LR

    subgraph DATA_PLANE["Data Plane — Complete Datasets"]
        direction TB

        A["Raw JSON artifact"]
        B["Processed CSV artifact"]
        C[("SQLite users table")]

        A --> B
        B --> C
    end

    subgraph CONTROL_PLANE["Control Plane — Airflow Metadata"]
        direction TB

        D["RawMetadata<br/>path, count, file size"]
        E["ValidationMetadata<br/>validated path and count"]
        F["ProcessedMetadata<br/>path, count, columns"]
        G["TableMetadata<br/>database path and table name"]
        H["LoadMetadata<br/>loaded count and load mode"]
        I["Final validation metadata<br/>expected and actual count"]

        D --> E
        E --> F
        F --> G
        G --> H
        H --> I
    end

    A -. "referenced by" .-> D
    A -. "validated through" .-> E
    B -. "referenced by" .-> F
    C -. "described by" .-> G
    C -. "load result" .-> H
    C -. "validated through" .-> I

    classDef data fill:#E8EEF7,stroke:#4A6280,color:#111827;
    classDef metadata fill:#E8F5E9,stroke:#2E7D32,color:#111827;

    class A,B,C data;
    class D,E,F,G,H,I metadata;

    style DATA_PLANE fill:#F5F8FC,stroke:#4A6280,stroke-width:2px
    style CONTROL_PLANE fill:#F4FBF5,stroke:#2E7D32,stroke-width:2px

This separation prevents Airflow's metadata database from being used as dataset storage and reduces coupling between task execution processes.

Each downstream task receives a serializable metadata reference through XCom and reads the corresponding persisted artifact only when the complete dataset is required.

The structure below focuses on source code, configuration, tests, orchestration, and documentation. Local or generated files—such as .env, .vscode/, .pytest_cache/, __pycache__/, execution logs, JSON and CSV artifacts, SQLite databases, and backup files—are omitted.

mini_pipeline_python/
│
├── airflow/
│   ├── config/
│   │   └── .gitkeep
│   │
│   ├── dags/
│   │   ├── minimal_airflow_validation.py
│   │   └── users_api_to_sqlite_pipeline.py
│   │
│   ├── plugins/
│   │   └── .gitkeep
│   │
│   ├── .env.example
│   └── docker-compose.yaml
│
├── data/
│   ├── database/
│   ├── processed/
│   └── raw/
│
├── logs/
│
├── src/
│   ├── __init__.py
│   ├── config.py
│   ├── contracts.py
│   ├── database.py
│   ├── extract.py
│   ├── load.py
│   ├── logger_config.py
│   ├── transform.py
│   └── validate.py
│
├── tests/
│   ├── test_database_load.py
│   ├── test_pipeline_functions.py
│   ├── test_processed_artifact.py
│   └── test_raw_validation.py
│
├── .dockerignore
├── .env.example
├── .gitignore
├── Dockerfile
├── environment.yml
├── main.py
├── pytest.ini
├── README.md
└── requirements.txt

• main.py executes the complete pipeline independently of Airflow.
• src/ contains extraction, validation, transformation, persistence, database, logging, configuration, and metadata-contract logic.
• tests/ contains automated tests for pipeline functions, artifacts, database loading, validation rules, and failure scenarios.
• airflow/dags/ contains the infrastructure-validation DAG and the six-task production-style pipeline DAG.
• airflow/docker-compose.yaml defines the local Airflow environment and its supporting services.
• data/ stores raw JSON files, processed CSV files, and the SQLite database generated during execution.
• logs/ stores standalone pipeline execution logs.
• environment.yml defines the reproducible Conda environment.
• requirements.txt declares Python dependencies used by containerized execution.

The project uses a dedicated Conda environment named mini_pipeline_python to isolate its Python version and dependencies.

From the project root, run:

conda env create -f environment.yml

conda activate mini_pipeline_python

python -c "import sys; print(sys.executable)"

The returned path should contain:

\anaconda3\envs\mini_pipeline_python\python.exe

python -c "import pandas, requests, dotenv, pytest; print('Dependencies: OK')"

The environment is ready when the project interpreter is active and all required dependencies are successfully imported.

Create a .env file in the project root based on .env.example.

On Windows PowerShell:

Copy-Item .env.example .env

On Linux, macOS, or Git Bash:

cp .env.example .env

Example content:

API_URL=https://jsonplaceholder.typicode.com/users
API_TIMEOUT_SECONDS=10

The .env file contains local configuration values and must not be committed to version control.

The pipeline can be executed independently of Airflow through main.py.

First, activate the project environment:

conda activate mini_pipeline_python

Then run the pipeline from the project root:

python main.py

A successful execution performs the following steps:

1. Extracts user data from the REST API.
2. Saves the raw dataset as a JSON artifact.
3. Validates the extracted records.
4. Transforms the data into the target structure.
5. Saves the processed dataset as a CSV artifact.
6. Creates the SQLite users table when necessary.
7. Loads the transformed records into SQLite.
8. Validates the final database record count.
9. Writes execution details to a log file.

The expected outputs are:

• A raw JSON file in data/raw/.
• A processed CSV file in data/processed/.
• A SQLite database in data/database/.
• An execution log in logs/.
• Ten consistent user records across the processed CSV and SQLite table.

Running the standalone pipeline validates the complete Python workflow independently of Airflow orchestration.

Activate the project environment:

conda activate mini_pipeline_python

Run the complete test suite from the project root:

python -m pytest tests -q

The automated tests cover:

• Successful validation and transformation.
• Invalid input types.
• Empty datasets and artifacts.
• Missing required fields.
• Null and duplicated identifiers.
• Corrupted JSON and CSV files.
• Invalid record counts.
• SQLite loading failures.
• Full-refresh and idempotency behavior.

A successful result should report that all tests passed.

Docker provides an isolated execution environment for the standalone Python pipeline.

From the project root:

docker build -t mini-pipeline-python .

docker run --rm --env-file .env mini-pipeline-python

The container and its generated files are removed after execution.

On Windows PowerShell:

docker run --rm --env-file .env `
  --mount type=bind,source="${PWD}\data",target=/app/data `
  --mount type=bind,source="${PWD}\logs",target=/app/logs `
  mini-pipeline-python

The bind mounts preserve generated data and logs on the host machine.

Each successful pipeline execution may generate:

These runtime artifacts are ignored by Git and are not committed to the repository.

The users table contains the following columns:

user_id
name
email
city
zipcode
latitude
longitude
company_name
processed_at

The processed_at field is stored in ISO 8601 format to preserve an unambiguous and sortable timestamp representation.

The project uses different validation levels because one successful execution does not prove that every pipeline layer works correctly.

Unit tests
→ validate isolated Python functions

Standalone execution
→ validates integration between the Python modules

Airflow DAG execution
→ validates orchestration and task dependencies

Repeated execution
→ validates idempotency

Artifact and record-count checks
→ validate consistency across pipeline stages

The expected record-count relationship is:

Raw records
=
Processed records
=
SQLite records

For the current JSONPlaceholder dataset, the expected result is ten user records.

During regression testing, standalone execution detected an integration mismatch: save_processed_data() expected a list of dictionaries, while main.py passed a pandas DataFrame.

The handoff was corrected so that the persistence function receives the transformed records before the DataFrame is created for the SQLite load.

The project includes a local Apache Airflow environment based on:

• Apache Airflow 3.3.0.
• Docker Compose.
• LocalExecutor.
• PostgreSQL as the Airflow metadata database.
• TaskFlow API.

The main DAG is:

users_api_to_sqlite_pipeline

It contains six tasks:

extract_and_save_raw_task
→ validate_raw_users_task
→ transform_and_save_processed_task
→ create_users_table_task
→ load_users_to_database_task
→ validate_database_load_task

The reusable pipeline logic remains in src/. Airflow coordinates task execution, dependencies, state, and operational behavior.

cd airflow

Copy-Item .env.example .env

Review the local values in .env, especially the administrator username and password.

docker compose up airflow-init

The initialization service should finish successfully with exit code 0.

docker compose up -d

Verify their status:

docker compose ps -a

Expected services include:

postgres
airflow-api-server
airflow-scheduler
airflow-dag-processor
airflow-triggerer

http://localhost:8080

The administrator credentials are defined in airflow/.env.

Confirm the installed version:

docker compose exec airflow-scheduler airflow version

Confirm the executor:

docker compose exec airflow-scheduler `
  airflow config get-value core executor

List the recognized DAGs:

docker compose exec airflow-scheduler airflow dags list

Check for DAG import errors:

docker compose exec airflow-scheduler `
  airflow dags list-import-errors --local

Validate the metadata database:

docker compose exec airflow-scheduler airflow db check

List the tasks in the main DAG:

docker compose exec airflow-scheduler `
  airflow tasks list users_api_to_sqlite_pipeline

In the Airflow interface:

1. Open users_api_to_sqlite_pipeline.
2. Enable the DAG when necessary.
3. Select Trigger DAG.
4. Wait for all six tasks to complete successfully.
5. Inspect task logs and XCom return values.

The XCom values must contain metadata and artifact references rather than complete datasets.

Stop the services without removing their containers:

docker compose stop

Restart the existing containers:

docker compose restart

Remove the containers while preserving the PostgreSQL volume:

docker compose down

Recreate them:

docker compose up -d

Do not use the following command when Airflow metadata must be preserved:

docker compose down --volumes

The --volumes option removes the persistent PostgreSQL volume.

The SQLite load uses a full-refresh strategy.

Before inserting the current dataset, the pipeline removes the existing target records. Repeated executions therefore replace the dataset instead of appending duplicates.

Expected behavior:

First execution  → 10 records
Second execution → 10 records

An incorrect append-based implementation would produce twenty records after the second execution.

The pipeline also applies explicit validation at important boundaries and fails with contextual error messages when data is missing, empty, corrupted, or inconsistent.

Completed:

• Modular standalone Python pipeline.
• REST API extraction.
• Raw JSON persistence.
• Data validation and transformation.
• Processed CSV persistence.
• SQLite loading and final validation.
• Logging and contextual error handling.
• Dedicated Conda environment.
• Dockerized standalone execution.
• Automated test suite.
• Negative and failure-scenario tests.
• Idempotent full-refresh loading.
• Local Airflow environment with Docker Compose.
• PostgreSQL metadata database.
• LocalExecutor configuration.
• Minimal Airflow validation DAG.
• Six-task pipeline DAG.
• Artifact-based communication between tasks.
• Metadata-only XCom contracts.
• Standalone and orchestrated regression validation.

Planned future improvements include:

1. Configure retries and retry delays.
2. Add task and execution timeouts.
3. Introduce scheduling and external parameters.
4. Add operational data-quality gates.
5. Implement alerts and failure notifications.
6. Manage connections and secrets through Airflow.
7. Practice backfills and historical reprocessing.
8. Improve monitoring and observability.
9. Evaluate PostgreSQL as a future pipeline destination.
10. Integrate dbt in a later transformation layer.
11. Evolve the pipeline toward Spark, PySpark, and Databricks.

This project was developed for educational and portfolio purposes.