Data Engineering for Artificial Intelligence Applications Training Course

 Data Engineering for Artificial Intelligence Applications Training Course 

Introduction 

Data Engineering for Artificial Intelligence Applications Training Course equips professionals with the technical knowledge and practical skills required to design, develop, deploy, and manage modern data engineering solutions that support artificial intelligence (AI), machine learning (ML), and generative AI applications. The course explores advanced data architecture, data integration, ETL and ELT pipelines, cloud data engineering, big data processing, data orchestration, real-time streaming, data quality management, and scalable data infrastructure. Participants will gain practical experience working with Python, SQL, Apache Spark, Apache Kafka, Databricks, Snowflake, and cloud-based data platforms to build reliable, automated, and high-performance data pipelines for AI-driven business environments. 

The training also examines how organizations can leverage data engineering to accelerate AI adoption, improve predictive analytics, strengthen intelligent automation, and support large language models (LLMs) through retrieval-augmented generation (RAG) and vector databases. Through practical exercises and global case studies, participants will explore enterprise data architectures, feature engineering, MLOps integration, responsible AI data governance, and scalable cloud-native solutions. The course emphasizes industry best practices, data security, interoperability, and operational efficiency, enabling professionals to transform fragmented data environments into intelligent, production-ready data ecosystems that drive innovation and digital transformation. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Design scalable data engineering architectures for AI applications.
2. Develop automated ETL and ELT pipelines for intelligent systems.
3. Implement Python and SQL for advanced data processing.
4. Apply Apache Spark for distributed data engineering.
5. Build real-time data streaming using Apache Kafka.
6. Integrate cloud data platforms with AI and ML workflows.
7. Implement feature engineering for machine learning models.
8. Develop vector database solutions for generative AI.
9. Integrate retrieval-augmented generation with enterprise data.
10. Apply MLOps principles to automated data pipelines.
11. Strengthen data quality, lineage, and observability.
12. Implement secure data governance and responsible AI practices.
13. Optimize data infrastructure for scalable AI deployment.

Organizational Benefits
 

1. Accelerates enterprise AI adoption and digital transformation.
2. Improves data pipeline efficiency and automation.
3. Reduces operational costs through optimized infrastructure.
4. Enhances machine learning model performance.
5. Strengthens data security and regulatory compliance.
6. Improves real-time business intelligence capabilities.
7. Supports scalable generative AI implementation.
8. Minimizes data inconsistencies and processing errors.
9. Strengthens enterprise data governance and management.
10. Improves innovation, productivity, and competitive advantage.

Target Audiences
 

1. Data Engineers and Data Architects.
2. Artificial Intelligence and Machine Learning Engineers.
3. Data Scientists and Analytics Professionals.
4. Cloud Engineers and Solutions Architects.
5. Database Administrators and Developers.
6. MLOps and DevOps Engineers.
7. IT Managers and Digital Transformation Leaders.
8. Business Intelligence and Technology Consultants.

Course Duration: 5 days

Course Modules

Module 1: Foundations of Data Engineering for AI
 

• Data engineering concepts, architectures, and AI ecosystems.
• Modern data platforms, data lakes, and lakehouse architectures.
• Structured, semi-structured, and unstructured data management.
• AI-ready data infrastructure and enterprise integration.
• Global case study: Netflix data infrastructure for recommendation systems.
• Practical exercise: Designing an AI-ready data architecture.

Module 2: Programming and Data Pipeline Development
 

• Python programming for scalable data engineering.
• SQL for advanced querying and data transformation.
• ETL and ELT pipeline development and automation.
• API integration and automated data ingestion.
• Global case study: Uber's data engineering and analytics ecosystem.
• Practical exercise: Building an automated Python data pipeline.

Module 3: Big Data Processing and Distributed Computing
 

• Apache Spark and distributed data processing.
• Databricks and large-scale data transformation.
• Batch processing and parallel computing techniques.
• Performance optimization and distributed storage.
• Global case study: LinkedIn's large-scale data processing infrastructure.
• Practical exercise: Processing large datasets using Apache Spark.

Module 4: Real-Time Data Streaming and Integration
 

• Apache Kafka and event-driven data architectures.
• Real-time data ingestion and stream processing.
• Data integration across enterprise applications.
• Event processing for intelligent AI applications.
• Global case study: Amazon's real-time data and recommendation ecosystem.
• Practical exercise: Developing a real-time streaming pipeline.

Module 5: Cloud Data Engineering and AI Infrastructure
 

• AWS, Microsoft Azure, and Google Cloud data services.
• Cloud data warehouses and scalable storage solutions.
• Data lakehouse implementation and management.
• Infrastructure automation and cloud pipeline orchestration.
• Global case study: Spotify's cloud-based data engineering ecosystem.
• Practical exercise: Designing a scalable cloud data platform.

Module 6: Feature Engineering and Machine Learning Data Pipelines
 

• Data preparation, cleansing, and feature extraction.
• Feature stores and reusable machine learning datasets.
• Data transformation for predictive analytics.
• Machine learning pipeline integration and automation.
• Global case study: Airbnb's machine learning data infrastructure.
• Practical exercise: Developing an end-to-end feature engineering pipeline.

Module 7: Generative AI, Vector Databases, and Retrieval-Augmented Generation
 

• Large language model data requirements and preparation.
• Vector databases, embeddings, and semantic search.
• Retrieval-augmented generation architecture and implementation.
• Document processing and knowledge retrieval pipelines.
• Global case study: Enterprise knowledge assistants using RAG architecture.
• Practical exercise: Building a document-based AI retrieval pipeline.

Module 8: MLOps, Data Governance, and AI Pipeline Optimization
 

• MLOps integration and automated model deployment pipelines.
• Data quality monitoring, observability, and lineage.
• Data governance, privacy, and responsible AI.
• Pipeline orchestration, reliability, and performance optimization.
• Global case study: Google's production machine learning infrastructure.
• Practical exercise: Designing a monitored and governed AI data pipeline.

Training Methodology
 

• Expert-led interactive presentations and technical discussions.
• Practical demonstrations using industry-standard data engineering tools.
• Hands-on Python, SQL, Apache Spark, and Kafka exercises.
• Real-world enterprise AI implementation case studies.
• Group assignments, collaborative problem-solving, and project development.
• Practical pipeline design, troubleshooting, and performance optimization.
• Knowledge assessments, participant presentations, and feedback sessions.

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.