Data Engineering for Analytics and AI Training Course

 Data Engineering for Analytics and Artificial Intelligence (AI) Training Course 

Introduction 

Data Engineering for Analytics and Artificial Intelligence (AI) Training Course is designed to equip participants with advanced technical knowledge and practical skills in modern data engineering, big data management, data integration, and analytics infrastructure. As organizations increasingly adopt artificial intelligence, machine learning, business intelligence, and data-driven decision-making, the demand for reliable, scalable, and high-performance data engineering solutions continues to grow. This course explores modern data architecture, data pipeline development, Extract, Transform, Load (ETL), Extract, Load, Transform (ELT), cloud data platforms, data warehousing, data lakes, data lakehouses, real-time data processing, and automated data workflows. Participants will gain practical exposure to Python, SQL, Apache Spark, Apache Kafka, and cloud-based data engineering technologies used to transform complex datasets into reliable analytical assets. 

The training further examines how enterprise data engineering supports predictive analytics, generative artificial intelligence, machine learning operations, and advanced business intelligence. Participants will learn to design scalable data pipelines, implement data quality frameworks, manage structured and unstructured data, optimize analytical workloads, and establish secure data governance practices. Through practical exercises and international case studies involving organizations such as Netflix, Amazon, Uber, and Google, participants will explore real-world applications of distributed computing, real-time analytics, cloud data integration, and artificial intelligence-ready data infrastructure. The course emphasizes industry best practices, emerging technologies, data security, automation, and performance optimization to help organizations develop resilient data ecosystems that support digital transformation and innovation. 

Course Objectives 

• Develop advanced data engineering and analytics infrastructure skills.
• Design scalable cloud-native data architectures.
• Implement automated ETL and ELT data pipelines.
• Apply Python and SQL for advanced data processing.
• Integrate big data technologies and distributed computing.
• Develop AI-ready datasets for machine learning applications.
• Implement real-time data streaming and processing.
• Optimize data warehouses and lakehouse architectures.
• Apply data quality, validation, and observability frameworks.
• Strengthen enterprise data governance and security.
• Integrate business intelligence and predictive analytics platforms.
• Implement machine learning data preparation workflows.
• Apply modern data engineering solutions to enterprise challenges.

Organizational Benefits
 

• Improved enterprise data management and accessibility.
• Faster data-driven strategic decision-making.
• Reduced data processing costs and operational inefficiencies.
• Enhanced artificial intelligence and machine learning capabilities.
• Improved data accuracy, consistency, and reliability.
• Scalable analytics and cloud computing infrastructure.
• Stronger cybersecurity and data governance.
• Improved real-time business intelligence capabilities.
• Increased automation of enterprise data workflows.
• Enhanced digital transformation and organizational competitiveness.

Target Audiences
 

1. Data engineers and data architects.
2. Data analysts and business intelligence professionals.
3. Artificial intelligence and machine learning engineers.
4. Database administrators and SQL developers.
5. Cloud computing and big data specialists.
6. Software developers and IT professionals.
7. Business intelligence managers and analytics consultants.
8. Chief Information Officers and digital transformation managers.

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of Data Engineering for Analytics and AI
 

• Data engineering principles and enterprise data ecosystems.
• Modern data architecture and analytical infrastructure.
• Structured, semi-structured, and unstructured data.
• Data lifecycle management and processing workflows.
• Integration of data engineering with AI and machine learning.
• Case study: Google's large-scale data infrastructure.

Module 2: Advanced Python and SQL for Data Engineering
 

• Python programming for data manipulation and automation.
• Advanced SQL queries and database optimization.
• Data transformation using Pandas and NumPy.
• Database integration and query performance tuning.
• Building reusable data processing scripts.
• Case study: Netflix data processing and analytics workflows.

Module 3: Big Data Processing and Distributed Computing
 

• Introduction to big data architecture and Hadoop ecosystems.
• Apache Spark for distributed data processing.
• Parallel computing and large-scale data transformation.
• Distributed storage and computational optimization.
• Batch processing and scalable analytics.
• Case study: Uber's distributed data processing infrastructure.

Module 4: Data Pipeline Development and Automation
 

• Designing enterprise data pipeline architectures.
• Developing ETL and ELT workflows.
• Data orchestration using Apache Airflow.
• Pipeline monitoring, scheduling, and automation.
• Error handling and workflow optimization.
• Case study: Amazon's automated data integration ecosystem.

Module 5: Cloud Data Engineering and Data Warehousing
 

• Cloud-based data engineering architecture.
• Data warehouse and data lake implementation.
• Modern lakehouse architecture and integration.
• Cloud data platforms including AWS, Azure, and Google Cloud.
• Storage optimization and analytical workload management.
• Case study: Google's BigQuery enterprise analytics applications.

Module 6: Real-Time Data Streaming and Integration
 

• Real-time data engineering architecture.
• Apache Kafka and event-driven data processing.
• Streaming analytics and message processing.
• API integration and data synchronization.
• Real-time monitoring and pipeline reliability.
• Case study: LinkedIn's Kafka-based data streaming ecosystem.

Module 7: Data Engineering for Artificial Intelligence and Machine Learning
 

• Preparing high-quality datasets for AI applications.
• Feature engineering and feature store development.
• Machine learning data pipelines and automation.
• Data integration for generative AI and large language models.
• Machine learning operations and model data management.
• Case study: Spotify's machine learning recommendation infrastructure.

Module 8: Data Governance, Quality, Security, and Optimization
 

• Enterprise data governance and management frameworks.
• Data quality assessment and automated validation.
• Data privacy, encryption, and access control.
• Data observability and pipeline performance monitoring.
• Infrastructure scalability and cost optimization.
• Case study: Microsoft's enterprise data governance practices.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical Python, SQL, and Apache Spark exercises.
• Hands-on enterprise data pipeline development.
• Cloud data engineering demonstrations and simulations.
• Group assignments involving real-world analytics challenges.
• International case study analysis and collaborative projects.
• Practical data integration and AI workflow demonstrations.
• Knowledge assessments, exercises, and participant feedback.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.