Building Your First End-to-End Batch Data Engineering Project

Are you a data analyst, student, or scientist looking to pivot into data engineering? Finding a comprehensive starter project that mirrors real-world complexity is often the biggest hurdle. This tutorial walks you through building a complete, end-to-end batch data pipeline designed to simulate an actual production environment, perfect for gaining hands-on experience and preparing for job interviews.

The Business Problem: User Behavior Metrics

Imagine you work for a user behavior analytics company. Your task is to build a robust data pipeline that ingests raw user data and populates a critical OLAP table: user_behavior_metric. This final table is consumed by analysts, dashboards, and other downstream applications.

Core Technologies for Our Pipeline

To execute this complex workflow efficiently, we utilize a powerful open-source stack:

• Apache Airflow: For defining, scheduling, and monitoring the data workflow (DAGs).
• Apache Spark: Essential for large-scale data processing and machine learning tasks, such as classifying user reviews.
• DuckDB: Used for fast, in-process analytical querying via SQL, generating metrics efficiently.
• Minio: An open-source object storage solution that acts as an S3 compatible storage layer (OSS S3).

For simplicity, these services are bundled and managed via containerization, allowing you to run the entire project quickly using GitHub Codespaces or locally.

The Data Pipeline: Extract, Transform, Load (ETL)

The user_behavior_metric data is derived from two primary datasets, processed through the following stages:

1. Extraction

Data extraction from source systems is handled efficiently using Airflow’s native operators. This stage ensures reliable fetching of raw data before processing begins.

2. Transformation and Metrics Generation

This is where the heavy lifting occurs. We leverage Spark for computationally intensive tasks, specifically implementing a naive Spark ML model for text classification (e.g., classifying user reviews as positive or negative). Calculated metrics, often generated using SQL executed via DuckDB, are then stored in a staging location (e.g., /opt/airflow/data/behaviour_metrics.csv).

3. Visualization and Dashboarding

The final stage involves presenting the processed data to consumers. We use quarto, a powerful tool that allows us to write Python code to generate dynamic HTML dashboards. Airflow’s BashOperator is utilized to trigger the dashboard creation, providing immediate insight into the newly calculated metrics.

Advanced Data Engineering Design Considerations

A successful project goes beyond just running the code. It requires careful design to handle failures, scale, and maintain data quality. After successfully running the user_analytics_dag, consider these critical real-world concepts:

Idempotent Data Pipelines

A core best practice is ensuring every task is idempotent. If a task fails and is re-run, the output should remain consistent. Review your pipeline—can you spot any tasks that might violate this principle?

Monitoring and Alerting

While the Airflow and Spark UIs offer basic monitoring, a production environment requires dedicated alerting for task failures, data quality issues, or hanging processes. Systems like Datadog, CloudWatch, or New Relic are commonly integrated here.

Data Quality Control

We did not implement data quality checks initially. In production, setting up checks (e.g., count validation, standard deviation checks) before loading the final table is crucial. Frameworks like great_expectations or lightweight solutions such as cuallee can enforce quality standards.

Concurrency and Backfills

If you need to re-run the pipeline for past periods (backfilling), concurrent execution is vital. However, review your DAG dependencies; even with appropriate concurrency settings, a blocking task might severely limit performance. Understanding how to manage backfills efficiently (rerunning only parts of the DAG versus the whole) is key to optimization.

Scaling for Growth

How would this architecture handle a 10x or 1000x increase in data volume? Scaling often requires moving beyond single-container setups and optimizing Spark configurations or shifting storage paradigms.

This project provides a comprehensive foundation. By tackling these design considerations, you transform a basic tutorial into a production-ready demonstration of your data engineering skills.

Reference: Inspired by content from https://www.startdataengineering.com/post/data-engineering-project-for-beginners-batch-edition/.