#ETL (13)
- Python Fundamentals: Data Engineering Primitives
This chapter establishes core Python programming concepts like data types, control flow, and functions, building an indispensable foundation for data manipulation tasks.
- NumPy & Pandas: Data Engineering Foundations
Building on Python fundamentals, this chapter introduces NumPy and Pandas. Master these essential libraries for efficient data loading, manipulation, and analysis, crucial for data engineering.
- Database Interaction: Python Connectors and CRUD
This chapter guides data engineers through connecting Python to relational databases, performing essential CRUD operations, and executing SQL queries programmatically for robust data management.
- Data Ingestion: How Python Reads Files and REST APIs
Master essential Python techniques for extracting data from diverse sources including CSV, JSON, XML files, and RESTful APIs, preparing it for robust data pipelines.
- Python Data Pipelines: Cleaning, Transformation, Validation
Learn to implement robust data cleaning, transformation, and validation techniques, ensuring high data quality and preparing datasets for analysis and storage.
- Cloud Platforms: Data Engineering Foundations
This chapter introduces core cloud computing principles, exploring how major providers like AWS, GCP, and Azure deliver essential data storage and processing services.
- Python ETL/ELT: How Modular Design Simplifies Flows
Design and implement robust Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipelines with Python, focusing on modularity for scalability and reusability.
- Apache Airflow: Orchestrating Data Pipelines with DAGs
Learn Apache Airflow fundamentals to define, schedule, and monitor complex data workflows using Directed Acyclic Graphs (DAGs), orchestrating robust data pipelines.
- Lakehouse Architectures: Principles and Python Implementation
This chapter explores data lake and lakehouse architectures, detailing their principles and demonstrating Python techniques for efficient storage and management of diverse, large-scale datasets.
- Real-time Data: Python for Streaming Ingestion
This chapter introduces the core concepts of real-time data processing, contrasting it with batch approaches and demonstrating basic streaming data ingestion patterns using Python and message queues.
- Cloud Data Warehouses: Python Integration Patterns
This chapter equips data engineers with Python skills to connect, manage, and bulk load data into cloud data warehouses like Snowflake, BigQuery, and Redshift, enabling robust pipelines.
- Ship Data Apps with Docker: Reproducible Environments
This chapter guides data engineers through packaging Python data applications and their dependencies into Docker containers, ensuring consistent, reproducible deployment environments from development to production.
- Python for Data Engineering - Zero to Mastery
Master Python for data engineering, transforming SQL expertise into production-ready skills for real-time projects, cloud integrations, and automated pipelines.