Data Engineering Learning Path

Estimated Total Duration: 12-18 months (studying 15-20 hours per week)

Milestone 1: Programming and Version Control

Duration: 2-3 months

Python Programming

• Data types and structures
• Functions and modules
• Object-oriented programming
• Exception handling
• File operations
• Virtual environments

SQL Fundamentals

• Basic queries and CRUD operations
• Joins and relationships
• Aggregations and grouping
• Subqueries
• Window functions
• Database design principles

Version Control

• Git basics
• Branching and merging
• Pull requests
• Collaborative workflows
• CI/CD concepts

Projects:

1. Data processing scripts
2. Database schema design
3. Multi-contributor Git project

Milestone 2: Data Storage Systems

Duration: 2-3 months

Relational Databases

• PostgreSQL deep dive
• MySQL administration
• Database optimization
• Indexing strategies
• Transaction management
• Backup and recovery

NoSQL Databases

• MongoDB
• Cassandra
• Redis
• Document vs. Column vs. Key-value stores
• CAP theorem
• Consistency patterns

Data Warehousing

• Dimensional modeling
• Star and snowflake schemas
• Slowly changing dimensions
• Fact table design
• Data marts
• Modern data warehouse architecture

Projects:

1. Database migration project
2. NoSQL application
3. Data warehouse design

Milestone 3: Data Processing and ETL

Duration: 3 months

ETL Framework

• Apache Airflow
• Data extraction techniques
• Transformation patterns
• Loading strategies
• Pipeline monitoring
• Error handling

Big Data Processing

• Apache Spark
• PySpark programming
• RDD and DataFrames
• Spark SQL
• Batch processing
• Stream processing basics

Data Quality

• Data validation
• Data cleaning
• Data profiling
• Testing strategies
• Documentation
• Data lineage

Projects:

1. ETL pipeline with Airflow
2. Spark data processing application
3. Data quality framework

Milestone 4: Cloud Platforms and Infrastructure

Duration: 2-3 months

AWS Services

• S3 and data lakes
• Redshift
• EMR
• Glue
• Athena
• IAM and security

Infrastructure as Code

• Terraform
• CloudFormation
• Infrastructure management
• Resource provisioning
• Configuration management
• Cost optimization

Containerization

• Docker basics
• Docker Compose
• Kubernetes fundamentals
• Container orchestration
• Microservices architecture
• Service mesh concepts

Projects:

1. Cloud data platform
2. IaC deployment
3. Containerized data pipeline

Milestone 5: Advanced Data Engineering

Duration: 2-3 months

Stream Processing

• Apache Kafka
• Real-time processing
• Event-driven architecture
• Stream analytics
• State management
• Fault tolerance

Data Architecture

• Lambda architecture
• Kappa architecture
• Data mesh
• Data fabric
• Modern data stack
• Data governance

Performance Optimization

• Query optimization
• Resource management
• Caching strategies
• Partitioning
• Distributed systems
• Monitoring and alerting

Projects:

1. Streaming data pipeline
2. Modern data architecture implementation
3. Performance optimization case study

Milestone 6: Production and Operations

Duration: 1-2 months

DevOps Practices

• CI/CD pipelines
• Automated testing
• Deployment strategies
• Monitoring tools
• Logging
• Alerting systems

Data Security

• Authentication
• Authorization
• Encryption
• Compliance (GDPR, CCPA)
• Security best practices
• Audit trails

System Design

• Scalability patterns
• High availability
• Disaster recovery
• Capacity planning
• Cost optimization
• Documentation

Projects:

1. Production-grade data pipeline
2. Security implementation
3. System design document

Final Capstone Project

Duration: 1-2 months

End-to-End Data Platform

• Requirements analysis
• Architecture design
• Implementation
• Testing and validation
• Documentation
• Presentation

Continuous Learning Elements

Best Practices

• Code review
• Documentation
• Testing strategies
• Security protocols
• Performance optimization
• Cost management

Tools and Technologies

• CLI tools
• Monitoring systems
• Debugging tools
• Profiling tools
• Development environments
• Cloud services

Professional Development

• Technical writing
• Project management
• Team collaboration
• Problem-solving
• Communication skills
• Industry trends

Assessment Criteria

Technical Competencies

• Code quality
• System design
• Problem-solving
• Performance optimization
• Security implementation
• Documentation

Project Deliverables

• Working systems
• Technical documentation
• Architecture diagrams
• Test coverage
• Performance metrics
• Security compliance