Python has become the backbone of modern data engineering due to its readability, rich ecosystem, and seamless integration with big data tools. This Python for Data Engineering course is designed to empower professionals with hands-on skills to build, automate, and scale robust data pipelines using Python. Participants will explore how to work with large datasets, develop ETL processes, interact with databases and APIs, and integrate Python with tools like Airflow, Spark, and cloud platforms. The course also covers data validation, transformation, and performance optimization, ensuring participants can deliver high-quality data solutions in real-time and batch environments.

Duration: 10 Days

Target Audience

• Aspiring and junior data engineers
• Python developers transitioning into data roles
• Data analysts seeking automation capabilities
• Data pipeline developers
• Software engineers working on data platforms
• Cloud engineers handling data workflows
• Technical data professionals in finance, health, and telecom sectors
• IT professionals building ETL and data integration solutions

Course Objectives

• Understand the role of Python in data engineering
• Build automated ETL/ELT pipelines using Python
• Interact with databases and APIs for data extraction
• Clean, validate, and transform large datasets
• Integrate Python scripts with workflow orchestration tools
• Work with cloud storage and processing services
• Leverage Python libraries like Pandas, SQLAlchemy, and PySpark
• Develop scalable and reusable code for data workflows
• Optimize data processing for performance and memory
• Implement testing, logging, and error handling in pipelines
• Enable real-time and batch processing with Python

Module 1: Introduction to Python for Data Engineering

• Overview of data engineering lifecycle
• Why Python is essential for data engineers
• Setting up the Python environment (venv, pip, IDEs)
• Introduction to Jupyter and script-based development
• Exploring Python packages for data workflows

Module 2: Python Programming Essentials

• Variables, data types, and control structures
• Functions and modules for reusable code
• File handling and exception management
• Working with JSON, CSV, XML data formats
• Introduction to object-oriented programming

Module 3: Working with Databases in Python

• Introduction to relational databases and SQL
• Connecting to MySQL/PostgreSQL using Python
• CRUD operations using SQLAlchemy and psycopg2
• Writing parameterized queries and transactions
• Handling schema changes and migration

Module 4: Data Extraction from APIs and Web Sources

• RESTful APIs and authentication (OAuth, Tokens)
• Making API calls using requests and httpx
• Parsing JSON and XML responses
• Handling rate limits and pagination
• Web scraping basics using BeautifulSoup and Selenium

Module 5: Data Cleaning and Transformation with Pandas

• Loading and exploring large datasets with Pandas
• Handling missing, duplicated, and incorrect values
• String manipulation and date parsing
• Merging, joining, and reshaping datasets
• Applying custom functions to dataframes

Module 6: Working with Big Data Using PySpark

• Introduction to Spark and PySpark
• Creating resilient distributed datasets (RDDs)
• DataFrames and SQL operations in PySpark
• Transformations and actions for large-scale data
• Writing to Parquet, Avro, and ORC formats

Module 7: Automating Workflows with Airflow

• Introduction to workflow orchestration
• DAGs, tasks, and operators in Apache Airflow
• Scheduling and monitoring data pipelines
• Integrating Python functions as Airflow tasks
• Logging and debugging Airflow workflows

Module 8: File and Data Storage Integration

• Reading and writing to local and network file systems
• Connecting with AWS S3, Google Cloud Storage, Azure Blob
• Managing large file uploads and downloads
• Chunking and streaming large datasets
• Organizing data lakes and directories

Module 9: Data Validation and Quality Checks

• Setting up data validation rules with Pandera and Cerberus
• Implementing schema checks and data profiling
• Detecting outliers, nulls, and duplicates
• Creating reusable validation modules
• Logging validation errors for review

Module 10: Unit Testing and Logging in Pipelines

• Writing test cases for data functions using pytest
• Mocking database/API calls for testability
• Logging best practices using Python logging module
• Implementing structured logs and error handling
• Creating test-driven data workflows

Module 11: Performance Optimization Techniques

• Identifying bottlenecks in Python scripts
• Vectorizing operations using NumPy and Pandas
• Memory profiling and garbage collection
• Lazy evaluation and generators
• Using multiprocessing and parallelism

Module 12: Cloud-Based Data Engineering with Python

• Using Python with AWS Lambda, GCP Cloud Functions
• Connecting to cloud data warehouses (BigQuery, Redshift, Snowflake)
• Automating cloud storage and compute tasks
• Deploying Python scripts as services or jobs
• Monitoring Python tasks in the cloud

Module 13: CI/CD for Python Data Pipelines

• Introduction to continuous integration/continuous delivery
• Using GitHub Actions or Jenkins for automation
• Building and testing pipelines with each commit
• Packaging and deploying Python modules
• Version control and rollback strategies

Module 14: Real-Time Data Processing Concepts

• Introduction to real-time vs batch processing
• Integrating Python with Kafka and Pub/Sub
• Event streaming basics with faust and confluent_kafka
• Writing Python consumers and producers
• Handling late-arriving and duplicated events

Module 15: Capstone Project: End-to-End Data Pipeline

• Design and implement a real-world pipeline
• Extract data from external APIs and databases
• Apply cleaning, transformation, and validation
• Orchestrate the workflow with Airflow
• Deploy the pipeline with monitoring and alerts

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.