08: Building Data Pipelines

1. Introduction to Data Pipelines

• 1.1 What is a Data Pipeline?
  • Definition and purpose
  • Real-world examples
• 1.2 Components of a Data Pipeline
  • Data sources
  • Ingestion
  • Transformation
  • Loading
  • Monitoring and management

2. ETL Processes (Extract, Transform, Load)

• 2.1 Extract
  • Data extraction techniques
  • Extracting data from various sources (APIs, databases, files)
• 2.2 Transform
  • Data cleaning and preprocessing
  • Data transformation methods and tools
  • Handling missing or inconsistent data
• 2.3 Load
  • Loading data into storage systems (databases, data warehouses)
  • Best practices for loading data efficiently

3. Designing and Architecting Data Pipelines

• 3.1 Design Principles
  • Scalability
  • Reliability
  • Maintainability
  • Performance considerations
• 3.2 Architecting Data Pipelines
  • Data pipeline design patterns
  • Choosing the right architecture for different scenarios (batch vs. streaming)
  • Using Python libraries and frameworks for pipeline design (e.g., Apache Airflow, Luigi)
• 3.3 Case Study
  • Real-world example of designing a data pipeline
  • Analyzing the design decisions and trade-offs

4. Implementing Data Ingestion, Transformation, and Loading (ETL)

• 4.1 Data Ingestion
  • Using Python to connect to data sources
  • Fetching and handling data from APIs, databases, and files
• 4.2 Data Transformation
  • Using Python libraries for data transformation (e.g., pandas, PySpark)
  • Writing transformation scripts and functions
• 4.3 Data Loading
  • Loading data into various storage systems using Python
  • Techniques for efficient data loading and updating
• 4.4 Building and Running Pipelines
  • Integrating ingestion, transformation, and loading into a complete pipeline
  • Scheduling and automating data pipelines with Python

5. Advanced Topics and Best Practices

• 5.1 Error Handling and Logging
  • Implementing error handling in data pipelines
  • Setting up logging and monitoring
• 5.2 Data Quality and Validation
  • Ensuring data quality throughout the pipeline
  • Implementing validation checks and tests
• 5.3 Security and Compliance
  • Securing data and pipeline processes
  • Compliance considerations and best practices

6. Project and Hands-On Practice

• 6.1 Project Overview
  • Designing and building a complete data pipeline
  • Requirements and objectives
• 6.2 Hands-On Labs
  • Implementing ETL processes using Python
  • Testing and optimizing data pipelines
• 6.3 Review and Feedback
  • Reviewing projects and providing feedback
  • Discussing challenges and solutions

7. Conclusion and Next Steps

• 7.1 Course Recap
  • Summary of key concepts and techniques
• 7.2 Further Learning Resources
  • Recommended books, tutorials, and tools
• 7.3 Career Pathways
  • Opportunities and career advice for data engineers