Advanced Python For Data Engineering Training Course: Big Data Python Mastery

Introduction

Elevate your data engineering skills with our Advanced Python for Data Engineering Training Course. This program is meticulously designed to equip you with the essential skills to master Python libraries for Big Data processing and analysis, enabling you to build robust and efficient data pipelines. In today's data-driven world, the ability to leverage Python for Big Data is crucial for handling massive datasets and driving actionable insights. Our Python training course provides hands-on experience and expert guidance, empowering you to build scalable and reliable data solutions.

This Big Data Python engineering training delves into the core concepts of advanced Python libraries, covering topics such as Spark with PySpark, Dask, and advanced Pandas techniques. You'll gain expertise in using industry-standard tools and techniques to process and analyze Big Data using Python, meeting the demands of modern data environments. Whether you're a data engineer, data scientist, or developer, this advanced Python course will empower you to build powerful data applications.

Target Audience:

• Data Engineers
• Data Scientists
• Big Data Developers
• Python Developers
• Software Engineers
• Data Architects
• Anyone needing advanced Python for data engineering skills

Course Objectives:

• Understand advanced Python libraries for Big Data processing.
• Master PySpark for distributed data processing.
• Utilize Dask for parallel computing and large datasets.
• Implement advanced Pandas techniques for data manipulation.
• Design and build efficient data pipelines using Python.
• Optimize Python code for performance and scalability.
• Troubleshoot and debug Python data engineering applications.
• Implement data security and access control in Python data workflows.
• Integrate Python with various Big Data platforms.
• Understand how to monitor and maintain Python data engineering systems.
• Explore advanced Python patterns and techniques for Big Data.
• Apply real world use cases for Python in data engineering.
• Leverage Python for data visualization within Big Data contexts.

Duration

10 Days

Course content

Module 1: Introduction to Advanced Python for Data Engineering

• Fundamentals of advanced Python for data engineering.
• Overview of Python libraries for Big Data processing.
• Setting up a Python data engineering development environment.
• Introduction to advanced Python concepts and techniques.
• Best practices for Python data engineering.

Module 2: PySpark for Distributed Data Processing

• Utilizing PySpark for distributed data processing.
• Implementing Spark DataFrames and SQL.
• Designing and building Spark pipelines.
• Optimizing Spark applications for performance.
• Best practices for PySpark.

Module 3: Dask for Parallel Computing

• Utilizing Dask for parallel computing and large datasets.
• Implementing Dask DataFrames and Arrays.
• Designing and building Dask workflows.
• Optimizing Dask applications for performance.
• Best practices for Dask.

Module 4: Advanced Pandas Techniques

• Utilizing advanced Pandas data manipulation techniques.
• Implementing efficient data aggregation and transformation.
• Optimizing Pandas code for large datasets.
• Utilizing Pandas for time series analysis.
• Best practices for advanced Pandas.

Module 5: Data Pipeline Design with Python

• Designing efficient data pipelines using Python.
• Utilizing Python libraries for data ingestion and transformation.
• Implementing data quality checks and validation.
• Automating data pipelines using Python.
• Best practices for data pipeline design.

Module 6: Performance Optimization and Scalability

• Optimizing Python code for performance.
• Utilizing profiling and benchmarking tools.
• Implementing parallel processing and concurrency.
• Designing scalable data applications.
• Best practices for performance optimization.

Module 7: Troubleshooting and Debugging

• Debugging Python data engineering applications.
• Analyzing performance and data issues.
• Utilizing debugging tools and techniques.
• Resolving common Python data engineering problems.
• Best practices for troubleshooting.

Module 8: Data Security and Access Control

• Implementing data security in Python data workflows.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Managing data permissions and privileges.
• Best practices for data security.

Module 9: Integration with Big Data Platforms

• Integrating Python with various Big Data platforms.
• Utilizing data connectors and APIs.
• Implementing data transfer between Python and Big Data systems.
• Best practices for integration.

Module 10: Monitoring and Maintenance

• Monitoring Python data engineering systems.
• Implementing alerting and notifications.
• Utilizing monitoring tools and techniques.
• Managing Python data applications.
• Best practices for monitoring.

Module 11: Advanced Python Patterns and Techniques

• Implementing asynchronous programming for data processing.
• Utilizing Python for data streaming and real-time analysis.
• Implementing Python for data visualization in Big Data.
• Advanced techniques for Python data engineering.
• Best practices for advanced patterns.

Module 12: Real-World Use Cases

• Implementing Python for ETL pipelines.
• Utilizing Python for data warehousing.
• Implementing Python for machine learning pipelines.
• Utilizing Python for real-time data analysis.
• Best practices for real world applications.

Module 13: Python and Cloud Environments

• Deploying Python data applications on cloud platforms.
• Utilizing cloud-based Python libraries and services.
• Optimizing cloud resources for Python data engineering.
• Best practices for cloud deployment.

Module 14: Python and Data Governance

• Implementing data governance policies in Python data workflows.
• Utilizing metadata management for Python data.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in Python for Data Engineering

• Emerging trends in Python for Big Data.
• Utilizing AI and automation in Python data pipelines.
• Implementing serverless Python data applications.
• Best practices for future Python data engineering.

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.