Scalable Clickstream Data Pipeline for User Behavior Analytics

A production-grade data pipeline for processing and analyzing user clickstream data at scale.

🏗️ Architecture

graph TD
    A[Kafka Producer] --> B[Kafka]
    B --> C[Spark Streaming]
    C --> D[Delta Lake]
    C --> E[Hudi]
    D --> F[Trino]
    E --> F
    F --> G[dbt]
    G --> H[Redshift]

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🛠️ Tech Stack

• Apache Kafka: Real-time event ingestion
• Apache Spark Structured Streaming: Stream processing
• Delta Lake & Apache Hudi: Dual-format data lake storage
• Hive Metastore + Trino: SQL-based query engine and catalog
• Airflow: Pipeline orchestration
• dbt: Data quality validation and schema checks
• Redshift: Downstream BI integration
• Terraform: AWS infrastructure provisioning

📁 Project Structure

.
├── terraform/                 # Infrastructure as Code
│   ├── modules/              # Reusable Terraform modules
│   └── environments/         # Environment-specific configurations
├── src/                      # Source code
│   ├── producer/            # Kafka producer
│   ├── streaming/           # Spark streaming jobs
│   └── batch/              # Batch processing jobs
├── dbt/                      # Data transformation
│   ├── models/             # dbt models
│   ├── tests/              # Data quality tests
│   └── analyses/           # Ad-hoc analyses
├── airflow/                  # Pipeline orchestration
│   └── dags/               # Airflow DAGs
└── config/                  # Configuration files

🚀 Getting Started

Prerequisites

• AWS Account with appropriate permissions
• Terraform v1.0+
• Python 3.8+
• Apache Spark 3.3+
• Apache Kafka 2.8+
• dbt 1.0+
• Airflow 2.0+

Infrastructure Setup

1. Initialize Terraform:

cd terraform/environments/dev
terraform init

2. Apply infrastructure:

terraform apply

Running the Pipeline

1. Start Kafka:

./scripts/start_kafka.sh

2. Start the producer:

python src/producer/producer.py

3. Start Spark streaming:

spark-submit src/streaming/streaming_job.py

4. Run batch processing:

spark-submit src/batch/batch_job.py

5. Run dbt tests:

cd dbt
dbt test

📊 Data Flow

1. Ingestion: Kafka producer generates synthetic clickstream events
2. Processing: Spark streaming job processes events in real-time
3. Storage: Data is stored in both Delta Lake and Hudi formats
4. Transformation: dbt models transform and validate the data
5. Analytics: Trino provides SQL access for analytics
6. BI: Aggregated data is synced to Redshift for BI tools

🔍 Data Quality

• Schema validation
• Data completeness checks
• Duplicate detection
• Anomaly detection
• Freshness monitoring

📈 Performance Tuning

Kafka

• Optimize partition count based on throughput
• Configure appropriate retention policies
• Monitor consumer lag

Spark

• Tune executor memory and cores
• Optimize shuffle partitions
• Configure appropriate batch intervals

Delta Lake

• Regular compaction
• Z-ordering for query performance
• Vacuum old files

Hudi

• Configure appropriate compaction strategy
• Optimize upsert performance
• Manage file sizes

🛠️ Monitoring

• Airflow task status and logs
• Spark UI for job monitoring
• Kafka metrics
• dbt test results
• Data quality dashboards

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Support

For support, please open an issue in the GitHub repository or contact the maintainers.