Python For Data Engineering Pipelines Training Course: Robust Data Pipelines

Introduction

Revolutionize your data engineering workflows with our Python for Data Engineering Pipelines Training Course. This program is designed to equip you with the essential skills to build robust data pipelines with Python libraries (Pandas, PySpark, etc.), enabling you to handle large-scale data processing and automation efficiently. In today's data-driven landscape, mastering Python for data engineering is crucial for building scalable and reliable data infrastructure. Our Python data engineering training course offers hands-on experience and expert guidance, empowering you to leverage powerful Python libraries for data pipeline development.

This robust data pipelines training delves into the core concepts of building efficient data pipelines using Python, covering topics such as data extraction, transformation, and loading (ETL), as well as distributed data processing. You'll gain expertise in using industry-standard Python libraries like Pandas and PySpark to build robust data pipelines with Python libraries (Pandas, PySpark, etc.), meeting the demands of modern data engineering projects. Whether you're a data engineer, data architect, or software developer, this Python for Data Engineering Pipelines course will empower you to design and implement high-performance data pipelines.

Target Audience:

• Data Engineers
• Data Architects
• Software Developers
• ETL Developers
• Data Scientists
• Database Administrators
• Anyone needing Python data engineering skills

Course Objectives:

• Understand the fundamentals of Python for data engineering pipelines.
• Master data extraction and transformation with Pandas.
• Utilize PySpark for distributed data processing and large-scale ETL.
• Implement data pipeline orchestration and automation with Python.
• Design and build robust data pipelines for real-world applications.
• Optimize data pipelines for performance, scalability, and maintainability.
• Troubleshoot and address common challenges in Python data pipelines.
• Implement data quality checks and validation in Python pipelines.
• Integrate Python pipelines with various data sources and destinations.
• Understand how to handle data storage and management in Python.
• Explore advanced Python libraries for data engineering (e.g., Dask, Airflow).
• Apply real world use cases for Python data pipelines.
• Leverage Python's ecosystem for efficient data engineering workflows.

Duration

10 Days

Course content

Module 1: Introduction to Python for Data Engineering Pipelines

• Fundamentals of Python for data engineering pipelines.
• Overview of Pandas, PySpark, and data pipeline orchestration.
• Setting up a Python data engineering development environment.
• Introduction to Python data engineering libraries and tools.
• Best practices for Python data pipelines.

Module 2: Data Extraction and Transformation with Pandas

• Mastering data extraction and transformation with Pandas.
• Utilizing Pandas data frames for data manipulation.
• Designing and building ETL processes with Pandas.
• Optimizing Pandas code for performance.
• Best practices for Pandas.

Module 3: Distributed Data Processing with PySpark

• Utilizing PySpark for distributed data processing and large-scale ETL.
• Implementing Spark data frames and transformations.
• Designing and building PySpark pipelines for big data.
• Optimizing PySpark jobs for cluster computing.
• Best practices for PySpark.

Module 4: Data Pipeline Orchestration and Automation

• Implementing data pipeline orchestration and automation with Python.
• Utilizing scheduling tools and workflow management.
• Designing and building automated data pipelines.
• Optimizing pipelines for reliability and scalability.
• Best practices for pipeline orchestration.

Module 5: Robust Data Pipeline Design

• Designing and building robust data pipelines for real-world applications.
• Implementing data validation and error handling.
• Utilizing modular and reusable code design.
• Optimizing pipelines for specific data engineering tasks.
• Best practices for pipeline design.

Module 6: Pipeline Optimization

• Optimizing data pipelines for performance, scalability, and maintainability.
• Utilizing performance tuning and profiling tools.
• Implementing data partitioning and caching strategies.
• Designing scalable pipeline architectures.
• Best practices for pipeline optimization.

Module 7: Troubleshooting Pipeline Challenges

• Debugging common challenges in Python data pipelines.
• Analyzing pipeline performance and errors.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common data engineering issues.
• Best practices for troubleshooting.

Module 8: Data Quality Checks and Validation

• Implementing data quality checks and validation in Python pipelines.
• Utilizing data validation libraries and techniques.
• Designing and building data quality monitoring systems.
• Optimizing validation for data integrity.
• Best practices for data quality.

Module 9: Integration with Data Sources and Destinations

• Integrating Python pipelines with various data sources and destinations.
• Utilizing database connections and APIs.
• Implementing data ingestion and export strategies.
• Optimizing integration for data retrieval and storage.
• Best practices for integration.

Module 10: Data Storage and Management

• Understanding how to handle data storage and management in Python.
• Utilizing file systems and cloud storage services.
• Implementing data partitioning and indexing.
• Designing efficient data storage solutions.
• Best practices for data storage.

Module 11: Advanced Python Data Engineering Libraries

• Exploring advanced Python libraries for data engineering (Dask, Airflow).
• Utilizing Dask for parallel data processing.
• Implementing Airflow for workflow orchestration.
• Designing and building advanced data engineering solutions.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced libraries.

Module 12: Real-World Use Cases

• Implementing Python pipelines for real-time data streaming.
• Utilizing Python pipelines for data warehousing and ETL.
• Implementing Python pipelines for data migration and integration.
• Utilizing Python pipelines for data quality management.
• Best practices for real-world applications.

Module 13: Python Data Engineering Tools Implementation

• Utilizing Python data engineering tools and frameworks.
• Implementing data pipelines with specific tools.
• Designing and building automated data workflows.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: Pipeline Performance Monitoring

• Implementing pipeline performance monitoring.
• Utilizing logging and monitoring tools.
• Designing and building performance dashboards.
• Optimizing monitoring for real-time insights.
• Best practices for monitoring.

Module 15: Future Trends in Python Data Engineering

• Emerging trends in Python data engineering.
• Utilizing AI for data pipeline automation.
• Implementing data pipelines in cloud-based environments.
• Best practices for future applications.

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.