Data Engineering Professional Certification

Module 1: Introduction to Data Engineering

• Data Engineering Fundamentals
• Data Engineering Lifecycle
• Data Ecosystem
• Modern Data Architecture
• Industry Applications

Module 2: Data Architecture

• Data Models
• Data Lakes
• Data Warehouses
• Lakehouse Architecture
• Enterprise Data Platforms

Module 3: SQL for Data Engineering

• Advanced SQL
• Query Optimization
• Window Functions
• Stored Procedures
• Performance Tuning

Module 4: Programming for Data Engineering

• Python Fundamentals
• Data Processing with Python
• File Handling
• APIs and Data Integration
• Automation Scripts

Module 5: ETL and ELT Pipelines

• ETL Fundamentals
• ELT Concepts
• Data Extraction
• Data Transformation
• Data Loading

Module 6: Apache Spark

• Spark Architecture
• DataFrames
• Spark SQL
• Distributed Processing
• Performance Optimization

Module 7: Hadoop Ecosystem

• HDFS
• MapReduce
• Hive
• HBase
• Hadoop Integration

Module 8: Apache Kafka

• Event Streaming
• Producers and Consumers
• Topics and Partitions
• Stream Processing
• Real-Time Pipelines

Module 9: Workflow Orchestration

• Workflow Scheduling
• Pipeline Automation
• Dependency Management
• Monitoring Jobs
• Error Recovery

Module 10: Cloud Data Engineering

• Cloud Storage
• Cloud Compute Services
• Managed Data Platforms
• Cloud Data Pipelines
• Deployment Strategies

Module 11: Data Warehousing

• Warehouse Design
• Dimensional Modeling
• Star Schema
• Snowflake Schema
• Warehouse Optimization

Module 12: Data Lakes

• Data Lake Architecture
• Structured Data
• Semi-Structured Data
• Unstructured Data
• Data Lake Governance

Module 13: NoSQL Databases

• Document Databases
• Key-Value Stores
• Column-Family Databases
• Graph Databases
• NoSQL Integration

Module 14: Data Quality

• Data Validation
• Data Cleansing
• Data Profiling
• Data Consistency
• Data Quality Monitoring

Module 15: Data Governance

• Data Governance Framework
• Metadata Management
• Data Lineage
• Data Catalogs
• Compliance Best Practices

Module 16: Data Security

• Authentication
• Authorization
• Encryption
• Access Control
• Secure Data Processing

Module 17: Performance Optimization

• Query Optimization
• Pipeline Optimization
• Partitioning
• Caching
• Resource Management

Module 18: Monitoring and Logging

• Pipeline Monitoring
• Logging Frameworks
• Performance Metrics
• Alerting
• Troubleshooting

Module 19: DevOps for Data Engineering

• Version Control
• CI/CD Pipelines
• Containerization
• Infrastructure Automation
• Deployment Best Practices

Module 20: Machine Learning Data Pipelines

• Data Preparation
• Feature Engineering
• Pipeline Integration
• Batch Processing
• Real-Time Processing

Module 21: Enterprise Data Engineering Best Practices

• Scalable Architecture
• Documentation
• Code Quality
• Collaboration
• Production Readiness

Module 22: Cloud-Based Data Engineering

• Multi-Cloud Concepts
• Data Migration
• Hybrid Architectures
• Cost Optimization
• Operational Excellence

Module 23: Real-Time Data Processing

• Streaming Pipelines
• Event-Driven Systems
• Stream Analytics
• Message Processing
• Low-Latency Architecture

Module 24: Data Engineering Capstone Project

• End-to-End Data Pipeline Development
• Cloud Deployment
• Data Warehouse Integration
• Performance Optimization
• Final Project Presentation