Description

Introduction

Python has become a go-to language for data engineers due to its simplicity, extensive libraries, and ability to handle diverse data engineering tasks. In this course, we will explore how Python can be used to automate and streamline various data workflows, ranging from data ingestion, transformation, and storage to orchestration and pipeline management. Python’s powerful libraries like Pandas, NumPy, and Apache Airflow make it an ideal tool for automating data workflows, saving time and resources.

You will learn to automate repetitive data tasks, create robust ETL (Extract, Transform, Load) pipelines, and manage large datasets efficiently. Additionally, we will explore how to integrate Python with cloud platforms and databases, and apply best practices for building scalable and maintainable data pipelines. Whether you’re working with structured or unstructured data, this course will prepare you to automate and optimize your data engineering tasks with Python.

Prerequisites

• Basic understanding of Python programming.
• Familiarity with databases (SQL or NoSQL).
• Knowledge of fundamental data engineering concepts (e.g., ETL processes, data pipelines).
• Experience with cloud services (optional but beneficial).

Table of Contents

1. Introduction to Data Engineering with Python
   1.1 Why Python for Data Engineering?
   1.2 Core Libraries and Tools for Data Engineering
   1.3 Overview of Data Engineering Workflows
   1.4 Key Concepts: ETL, Data Pipelines, and Orchestration
2. Setting Up the Python Environment for Data Engineering
   2.1 Installing Python and Necessary Libraries
   2.2 Working with Virtual Environments and Dependency Management
   2.3 Setting Up IDEs and Jupyter Notebooks for Data Workflows
   2.4 Version Control: Git for Managing Data Engineering Projects
3. Data Ingestion with Python
   3.1 Extracting Data from APIs and Web Scraping
   3.2 Ingesting Data from Databases using Python
   3.3 Loading Data from Cloud Storage (AWS S3, Google Cloud Storage)
   3.4 Handling Streaming Data with Python and Kafka
4. Data Transformation and Cleaning
   4.1 Data Wrangling with Pandas: Merging, Cleaning, and Reshaping Data
   4.2 Transforming Data with NumPy and Pandas
   4.3 Handling Missing Data and Duplicates
   4.4 Working with Unstructured Data: Text and JSON Processing
5. Building ETL Pipelines in Python
   5.1 Designing ETL Pipelines: Extract, Transform, Load
   5.2 Creating Reusable ETL Functions and Modular Code
   5.3 Batch vs. Stream Processing in ETL
   5.4 Handling Errors and Data Validation in ETL Pipelines
6. Data Storage and Integration
   6.1 Loading Data into Relational Databases (SQL)
   6.2 Storing Data in NoSQL Databases (MongoDB, Cassandra)
   6.3 Using Cloud Databases (AWS RDS, Azure SQL Database)
   6.4 Integrating Data with Data Lakes and Warehouses
7. Orchestrating Data Pipelines with Apache Airflow
   7.1 Introduction to Apache Airflow for Workflow Automation
   7.2 Setting Up Airflow and Creating DAGs (Directed Acyclic Graphs)
   7.3 Automating Data Pipelines with Airflow Operators
   7.4 Scheduling and Monitoring Workflows in Airflow
8. Optimizing and Scaling Data Pipelines
   8.1 Performance Tuning for Data Workflows
   8.2 Using Parallelism and Distributed Processing in Python
   8.3 Scaling ETL Pipelines with Apache Spark and Dask
   8.4 Managing Large Datasets and Optimizing Data Storage
9. Cloud Data Engineering with Python
   9.1 Introduction to Cloud Services (AWS, Azure, GCP)
   9.2 Automating Data Pipelines on Cloud Platforms
   9.3 Using Cloud Storage and Databases with Python
   9.4 Integrating Python with Cloud Analytics and Big Data Tools
10. Best Practices for Python Data Engineering
    10.1 Writing Clean, Maintainable, and Testable Code
    10.2 Error Handling and Logging in Data Pipelines
    10.3 Optimizing Data Workflow Performance
    10.4 Version Control and Collaboration in Data Engineering

Conclusion

By completing this course, you will have gained hands-on experience automating data workflows using Python, making you proficient in creating, optimizing, and scaling data pipelines. Python’s flexibility and powerful libraries allow data engineers to streamline repetitive tasks, handle complex data processing, and ensure seamless integration with databases and cloud platforms.

With the skills you’ve acquired in data ingestion, transformation, storage, and pipeline orchestration, you’ll be prepared to tackle real-world challenges in data engineering. You’ll also be equipped with the best practices necessary to build scalable, efficient, and reliable data systems. Whether you’re working in small teams or large enterprise environments, Python will serve as a critical tool in automating your data workflows and driving efficiency across your data engineering projects.