Data Engineering Bible

Welcome to the Data Engineering Bible, a comprehensive resource covering the fundamentals, best practices, and advanced concepts in data engineering.

Table of Contents

1. Introduction to Data Engineering

• Overview of Data Engineering
• Roles and Responsibilities of Data Engineers
• Evolution of Data Engineering

2. Data Lifecycle Management

• Overview of Data Lifecycle
• Data Storage Solutions
• Data Processing and Transformation
• Data Archival and Lifecycle Policies

3. Data Warehousing Concepts

• Fundamentals of Data Warehousing
• Modern Trends in Data Warehousing
• Comparison of Data Warehousing Tools

4. Data Lakes and Lakehouse Architectures

• Introduction to Data Lakes
• Challenges with Traditional Data Lakes
• Lakehouse Architecture Principles
• Medallion Architecture
• Data Vault Modeling

5. Data Ingestion and Processing

• Batch Data Ingestion
• Near Real-Time Data Ingestion
• Real-Time Data Ingestion
• ETL vs ELT

6. Data Governance and Quality

• Introduction to Data Governance
• Ensuring Data Quality
• Data Security Best Practices
• Data Lineage
• Master Data Management
• Best Practices in Governance

7. File Formats and Storage

• Overview of Common Data Formats
• Comparing JSON, Avro, and Parquet
• Advantages and Disadvantages of Formats

8. Cloud Databases and Distributed Systems

• Introduction to Cloud Databases
• Distributed Systems and Scalability
• Cost Optimization Strategies

9. Advanced Architectures

• Data Mesh Concepts
• Data Fabric Principles
• Vector Databases
• Semantic Layer for Analytics
• AI/ML Integration in Data Pipelines

10. Workflows and Orchestration

• Overview of Workflow Orchestration
• Comparison of Orchestration Tools
• Best Practices in Orchestration

11. Case Studies and Tutorials

• Setting Up a Data Lakehouse
• Kafka Streaming Pipeline
• Data Governance Dashboard
• Real-Time Analytics Use Case

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