Enterprise-grade data engineering platform with batch & streaming processing, comprehensive monitoring, data governance, and ML integration
A comprehensive, production-ready data pipeline that seamlessly integrates batch and streaming processing with enterprise-grade monitoring, governance, and machine learning capabilities.
Batch Sources: MySQL, PostgreSQL, CSV/JSON/XML files, Data Lakes (MinIO/S3)
Streaming Sources: Apache Kafka for event logs, IoT sensor data, social media streams,
and real-time CDC (Change Data Capture)
Batch Processing: Apache Spark for large-scale ETL with Great Expectations for data quality
Stream Processing: Spark Structured Streaming for real-time transformations,
anomaly detection, and event processing
Multi-tier Architecture: MinIO/S3 for raw data lake, PostgreSQL for analytics, MongoDB for documents, InfluxDB for time-series, Redis for caching, Elasticsearch for search
Observability: Prometheus for metrics, Grafana for dashboards, ELK stack for logs
Governance: Apache Atlas/OpenMetadata for lineage, Great Expectations for quality
MLOps: MLflow for experiment tracking and model registry
Feature Store: Feast for feature management and serving
BI Tools: Integration with Tableau, Power BI, Looker
GitOps: Argo CD for continuous delivery
Container Orchestration: Kubernetes with Helm charts
IaC: Terraform for cloud infrastructure
Strategies: Blue/Green, Canary, Rolling deployments
Cloud-native, microservices-based architecture designed for scalability, reliability, and maintainability
Built with industry-leading open-source technologies and cloud-native tools
Enterprise-grade capabilities for production data engineering workloads
Process millions of events per second with Spark Structured Streaming and Kafka. Sub-second latency for critical business insights with exactly-once semantics.
Scalable batch processing with Apache Spark. Handle petabyte-scale datasets with optimized partitioning, compression, and distributed computing.
Automated validation with Great Expectations. Define expectations, run validations, and generate data quality reports. Prevent bad data from entering your pipeline.
Track data flow from source to destination with Apache Atlas. Understand data dependencies, impact analysis, and compliance with automated lineage tracking.
Comprehensive monitoring with Prometheus and Grafana. Track pipeline health, performance metrics, SLA compliance, and receive intelligent alerts.
Enterprise security with encryption at rest and in transit. RBAC, audit logging, secrets management, and compliance with GDPR, HIPAA, SOC 2.
Fully containerized with Docker and Kubernetes. Portable, scalable, and cloud-agnostic. Run on AWS, GCP, Azure, or on-premises infrastructure.
GitOps workflow with Argo CD. Automated testing, deployment, and rollback. Blue/Green and Canary deployment strategies with progressive delivery.
Seamless MLOps with MLflow and Feast. Track experiments, manage models, serve predictions, and maintain feature stores for ML workflows.
Enterprise-grade deployment patterns for zero-downtime releases
Deploy new version alongside current version, then switch traffic instantly. Instant rollback capability by switching back to previous version.
Gradually shift traffic from old to new version with automated analysis. Detect issues early and rollback automatically if metrics degrade.
Update pods incrementally while maintaining service availability. Ideal for stateless services with minimal resource requirements.
Real-world applications across industries and domains
Real-time recommendations, fraud detection, inventory optimization, customer behavior analysis, and personalized marketing campaigns.
Risk analysis, trade surveillance, fraud detection, regulatory compliance, portfolio analytics, and real-time transaction monitoring.
Patient monitoring, clinical trial analysis, predictive diagnostics, IoT medical device data processing, and treatment outcome prediction.
Predictive maintenance, supply chain optimization, quality control, sensor data analysis, and production efficiency monitoring.
Sentiment analysis, ad fraud detection, content recommendations, user engagement tracking, and real-time trend detection.
Route optimization, fleet management, demand forecasting, delivery tracking, and real-time traffic analysis.
Set up the entire pipeline in minutes with Docker Compose
Get the source code from GitHub and navigate to the project directory
git clone https://github.com/hoangsonww/End-to-End-Data-Pipeline.git
cd End-to-End-Data-PipelineLaunch all services with Docker Compose. This will start MySQL, PostgreSQL, Kafka, MinIO, Airflow, Spark, Prometheus, and Grafana
docker-compose up --buildOnce all services are running, access the web interfaces:
Enable and trigger the batch ingestion DAG in Airflow to see the end-to-end pipeline in action
# In Airflow UI, enable batch_ingestion_dag
# Or trigger via CLI:
docker-compose exec airflow airflow dags trigger batch_ingestion_dagStart the Kafka producer and Spark streaming job for real-time processing
# Start Kafka producer
docker-compose exec kafka python /opt/spark_jobs/../kafka/producer.py
# Run Spark streaming job
docker-compose exec spark spark-submit --master local[2] \
/opt/spark_jobs/spark_streaming_job.pyFor production deployment, use Kubernetes with Argo CD for GitOps
# Apply Kubernetes manifests
kubectl apply -f kubernetes/
# Setup Argo CD
kubectl apply -f kubernetes/argo-app.yaml
# Deploy with Terraform (for cloud infrastructure)
cd terraform
terraform init
terraform apply