Data Engineering Best Practices & Design Patterns Training Course: Robust Data Systems

Introduction

Elevate your data engineering expertise with our Data Engineering Best Practices and Design Patterns Training Course. This program is designed to equip you with the essential knowledge to implement best practices for building robust data systems, ensuring your data infrastructure is scalable, maintainable, and efficient. In today's data-driven world, mastering data engineering best practices is crucial for organizations seeking to build reliable and high-performance data platforms. Our data engineering training course offers hands-on experience and expert guidance, empowering you to design and implement data systems that meet the demands of modern data workloads.

This robust data systems training delves into the core concepts of data engineering design patterns and best practices, covering topics such as data modeling, pipeline architecture, and data governance. You'll gain expertise in using industry-standard techniques to implement best practices for building robust data systems, meeting the demands of modern data-intensive environments. Whether you're a data engineer, data architect, or data scientist, this Data Engineering Best Practices and Design Patterns course will empower you to build and maintain efficient and scalable data solutions.

Target Audience:

• Data Engineers
• Data Architects
• Data Scientists
• Big Data Engineers
• Backend Developers
• Cloud Engineers
• Anyone needing data engineering best practices skills

Course Objectives:

• Understand the fundamentals of data engineering best practices and design patterns.
• Master data modeling and schema design for robust data systems.
• Utilize data pipeline architecture patterns for efficient data processing.
• Implement data governance and quality assurance best practices.
• Design and build scalable data storage and retrieval systems.
• Optimize data engineering workflows for performance and reliability.
• Troubleshoot and address common issues in data engineering implementations.
• Implement data security and compliance best practices.
• Integrate data engineering best practices with various data platforms.
• Understand how to handle large-scale data processing with best practices.
• Explore advanced data engineering design patterns (e.g., Lambda Architecture, Data Mesh).
• Apply real world use cases for data engineering best practices.
• Leverage data engineering tools and frameworks for efficient implementation.

Duration

10 Days

Course content

Module 1: Introduction to Data Engineering Best Practices

• Fundamentals of data engineering best practices and design patterns.
• Overview of data modeling, pipeline architecture, and data governance.
• Setting up a data engineering development environment.
• Introduction to key design patterns and principles.
• Best practices for data engineering foundations.

Module 2: Data Modeling and Schema Design

• Mastering data modeling and schema design for robust data systems.
• Utilizing normalization and denormalization techniques.
• Implementing schema evolution strategies.
• Designing and building efficient data schemas.
• Best practices for data modeling.

Module 3: Data Pipeline Architecture

• Utilizing data pipeline architecture patterns for efficient data processing.
• Implementing ETL and ELT pipelines.
• Designing and building scalable data pipelines.
• Optimizing pipelines for performance and reliability.
• Best practices for pipeline architecture.

Module 4: Data Governance and Quality Assurance

• Implementing data governance and quality assurance best practices.
• Utilizing data quality checks and validation.
• Designing and building data governance frameworks.
• Optimizing data quality for trust and compliance.
• Best practices for data governance.

Module 5: Scalable Data Storage and Retrieval

• Designing and building scalable data storage and retrieval systems.
• Utilizing distributed storage and database systems.
• Implementing data partitioning and sharding.
• Optimizing storage for query performance.
• Best practices for scalable storage.

Module 6: Workflow Optimization

• Optimizing data engineering workflows for performance and reliability.
• Utilizing workflow orchestration tools.
• Implementing automation and monitoring.
• Designing efficient data processing workflows.
• Best practices for workflow optimization.

Module 7: Troubleshooting Data Engineering Implementations

• Troubleshooting and addressing common issues in data engineering implementations.
• Analyzing data pipeline logs and error messages.
• Utilizing problem-solving techniques for resolution.
• Resolving common data engineering errors.
• Best practices for troubleshooting.

Module 8: Data Security and Compliance

• Implementing data security and compliance best practices.
• Utilizing data encryption and access control.
• Designing and building secure data systems.
• Optimizing security for data protection.
• Best practices for security.

Module 9: Integration with Data Platforms

• Integrating data engineering best practices with various data platforms.
• Utilizing APIs and data connectors.
• Implementing best practices for cloud and on-premises systems.
• Optimizing integration for data processing.
• Best practices for integration.

Module 10: Large-Scale Data Processing

• Understanding how to handle large-scale data processing with best practices.
• Utilizing distributed computing frameworks.
• Implementing data sharding and parallel processing.
• Designing scalable data processing solutions.
• Best practices for large data.

Module 11: Advanced Data Engineering Patterns

• Exploring advanced data engineering design patterns (Lambda Architecture, Data Mesh).
• Utilizing Lambda Architecture for real-time and batch processing.
• Implementing Data Mesh for decentralized data ownership.
• Designing and building advanced data architectures.
• Optimizing advanced patterns for specific applications.
• Best practices for advanced patterns.

Module 12: Real-World Use Cases

• Implementing best practices for building data lakes and lakehouses.
• Utilizing design patterns for real-time data analytics.
• Implementing best practices for machine learning data pipelines.
• Utilizing design patterns for data warehousing and ETL.
• Best practices for real-world applications.

Module 13: Data Engineering Tools Implementation

• Utilizing data engineering tools and frameworks (Airflow, Spark, Delta Lake).
• Implementing best practices with specific tools.
• Designing and building automated workflows.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: Performance Monitoring and Metrics

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

Module 15: Future Trends in Data Engineering

• Emerging trends in data engineering best practices.
• Utilizing AI for data engineering automation.
• Implementing data engineering in cloud-native 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.