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🔬 Data Engineer - Complete Guide

Specialized guide for Data Engineers at Appgain

🎯 Role Overview

Data Engineers at Appgain build and maintain data pipelines, ensure data quality, and provide analytics infrastructure for business intelligence and machine learning.

📚 Specialized Learning

Required Courses

Additional Resources

🛠️ Data Infrastructure

n8n Workflow Automation Server

  • URL: https://n8n.instabackend.io/
  • Purpose: Workflow automation and data integration
  • Access: Available for AI, Support, and Data Engineers
  • Features:
  • Data Pipeline Automation: Create automated ETL/ELT workflows
  • Multi-source Integration: Connect various data sources and APIs
  • Real-time Data Processing: Stream data through automated workflows
  • Custom Triggers: Set up automated triggers for data processing
  • Data Transformation: Built-in data transformation capabilities
  • Error Handling: Robust error handling and retry mechanisms

Data Pipeline Tools

  • n8n Server: Workflow automation and data integration platform
  • Apache Spark: Big data processing
  • Jupyter Notebooks: Data analysis and experimentation
  • Cron Jobs: Scheduled data processing tasks

Data Storage

  • MongoDB: Primary operational data and analytics
  • Redis: Caching and real-time data
  • PostgreSQL: KONG API Gateway configuration (only)
  • AWS S3: Data lake and archival storage

🔧 Key Responsibilities

1. Data Pipeline Development

  • ETL/ELT Processes: Build and maintain data pipelines
  • Data Integration: Connect various data sources
  • Data Transformation: Clean and transform raw data
  • Data Quality: Ensure data accuracy and consistency

2. Analytics Infrastructure

  • Data Warehouse: Maintain analytics data warehouse
  • Reporting Systems: Build reporting and dashboard infrastructure
  • Data APIs: Create APIs for data access
  • Performance Optimization: Optimize query performance

3. Data Governance

  • Data Catalog: Maintain data documentation and metadata
  • Access Control: Manage data access permissions
  • Compliance: Ensure data privacy and compliance
  • Backup Strategy: Implement data backup and recovery

🚀 Technical Stack

Core Technologies

  • Python: Primary data processing language
  • SQL: Database querying and manipulation
  • Apache Spark: Distributed data processing
  • n8n Server: Workflow automation and data integration
  • Jupyter: Interactive data analysis

Development Tools

  • Git: Version control for data pipelines
  • Docker: Containerization for data services
  • Postman: API testing for data endpoints
  • Monitoring: Prometheus/Grafana for data metrics

📊 Success Metrics

Data Quality Metrics

  • Data Accuracy: > 99% data accuracy
  • Pipeline Reliability: > 99.9% pipeline uptime
  • Processing Speed: < 1 hour for daily data processing
  • Data Freshness: < 15 minutes for real-time data

Performance Metrics

  • Query Performance: < 30 seconds for complex queries
  • Storage Efficiency: Optimized data storage usage
  • Cost Optimization: Minimized data processing costs
  • Scalability: Handle 10x data volume growth

🔗 Integration Points

Data Sources

  • Appgain Server: User and application data
  • Parse Server: Multi-tenant application data
  • Notify Service: Communication and engagement data
  • External APIs: Third-party data integrations

Data Consumers

  • Growth Machine: Analytics and reporting
  • Admin Server: Business intelligence dashboards
  • AI Models: Machine learning data feeds
  • Business Teams: Data-driven decision making

📋 Daily Operations

Morning Routine

# Check data pipeline status
curl https://n8n.instabackend.io/health

# Monitor data processing jobs
spark-submit --class DataQualityCheck /opt/jobs/data-quality.jar

# Check database health
mongo --eval "db.stats()"
psql -c "SELECT count(*) FROM analytics.events;"

Development Workflow

# Start Jupyter environment
jupyter notebook --ip=0.0.0.0 --port=8888

# Run data pipeline
python scripts/run_etl_pipeline.py

# Test data transformations
pytest tests/test_data_transformations.py

# Deploy data changes
git push origin main

Monitoring & Maintenance

# Monitor data pipeline metrics
curl http://monitor.instabackend.io:9090/a../query?query=data_pipeline_duration

# Check data quality
python scripts/data_quality_check.py

# Backup critical data
bash scripts/backup_analytics_data.sh

# Clean up old data
python scripts/data_cleanup.py

🎯 Project Examples

1. User Analytics Pipeline

  • Goal: Comprehensive user behavior analytics
  • Technology: n8n + Spark + PostgreSQL
  • Integration: Growth Machine
  • Metrics: User engagement, conversion rates

2. Real-time Data Processing

  • Goal: Real-time event processing and analytics
  • Technology: Spark Streaming + Redis
  • Integration: Notify Service
  • Metrics: Event processing latency, throughput

3. Data Lake Implementation

  • Goal: Centralized data storage and processing
  • Technology: AWS S3 + Spark + n8n
  • Integration: All data sources
  • Metrics: Data accessibility, processing efficiency

🔧 Troubleshooting

Common Issues

  • Pipeline Failures: Check data source connectivity
  • Performance Issues: Monitor resource utilization
  • Data Quality: Validate data transformations
  • Storage Issues: Check disk space and quotas

Debug Commands

# Check pipeline status
curl https://n8n.instabackend.io/health

# Monitor Spark jobs
spark-submit --class JobMonitor /opt/jobs/monitor.jar

# Validate data quality
python scripts/validate_data.py

# Check database performance
EXPLAIN ANALYZE SELECT * FROM analytics.events;

📚 Learning Path

Week 1: Foundation

  • Complete data engineering courses
  • Set up development environment
  • Understand data architecture
  • Learn basic data processing

Week 2: Hands-on

  • Build first data pipeline
  • Set up monitoring
  • Create data quality checks
  • Learn data visualization

Week 3: Advanced

  • Optimize data processing
  • Implement data governance
  • Create analytics dashboards
  • Document data processes

Week 4: Production

  • Deploy to production
  • Monitor and maintain
  • Optimize for scale
  • Share knowledge with team

🎥 Video Resources & Tutorials

Data Engineer Training Videos

Data Engineer Training

🎯 Quick Navigation

📊 Data Engineers build and maintain the data infrastructure that powers our analytics, insights, and AI capabilities across the entire platform.

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