Data Engineering for Generative AI Applications Training Course

 Data Engineering for Generative AI Applications Training Course 

Introduction 

Data Engineering for Generative Artificial Intelligence Applications Training Course is designed to equip participants with the technical knowledge and competencies required to build, manage, optimize, and scale modern data infrastructure for Generative Artificial Intelligence (GenAI) solutions. As organizations increasingly adopt Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), intelligent automation, and AI-powered applications, the demand for reliable, scalable, secure, and high-performance data engineering architectures continues to grow. This course explores modern data engineering technologies, cloud-native data platforms, data pipelines, vector databases, real-time data processing, data orchestration, and distributed computing frameworks. Participants will learn how to transform structured and unstructured data into AI-ready assets that support model training, fine-tuning, inference, and enterprise Generative AI deployment. 

The training emphasizes practical implementation of end-to-end data engineering workflows using technologies such as Apache Spark, Apache Kafka, Apache Airflow, Python, SQL, Databricks, Snowflake, cloud data warehouses, vector databases, and modern lakehouse architectures. Participants will explore data ingestion, transformation, validation, metadata management, embedding generation, semantic search, and knowledge retrieval for enterprise AI applications. International industry case studies will demonstrate how organizations leverage scalable data infrastructure to develop intelligent applications, improve operational efficiency, and accelerate digital transformation. By the end of the course, participants will be equipped to design production-ready data engineering solutions that support secure, reliable, and scalable Generative AI ecosystems. 

Course Objectives 

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

1. Design scalable data engineering architectures for Generative AI applications.
2. Develop automated data ingestion and transformation pipelines.
3. Implement Retrieval-Augmented Generation data workflows.
4. Integrate structured and unstructured enterprise datasets.
5. Apply Apache Spark for distributed data processing.
6. Build real-time data pipelines using Apache Kafka.
7. Implement vector databases and embedding management.
8. Develop AI-ready data lakes and lakehouse architectures.
9. Apply data quality, validation, and governance frameworks.
10. Optimize data pipelines for LLM training and inference.
11. Implement metadata management and semantic search.
12. Deploy cloud-native data engineering infrastructure.
13. Apply security, scalability, and MLOps principles to enterprise AI data systems.

Organizational Benefits
 

1. Accelerates enterprise Generative AI adoption and digital transformation.
2. Improves data accessibility and operational efficiency.
3. Reduces data preparation costs and processing bottlenecks.
4. Strengthens enterprise data governance and security.
5. Improves the accuracy and reliability of AI-generated outputs.
6. Supports scalable Retrieval-Augmented Generation implementation.
7. Enhances real-time business intelligence and decision-making.
8. Improves collaboration between data engineering and AI teams.
9. Enables better utilization of enterprise data assets.
10. Strengthens organizational competitiveness through AI innovation.

Target Audiences
 

1. Data engineers and data architects.
2. Artificial intelligence and machine learning engineers.
3. Generative AI developers and application architects.
4. Cloud engineers and infrastructure specialists.
5. Database administrators and data warehouse professionals.
6. Business intelligence and analytics professionals.
7. MLOps engineers and technology consultants.
8. IT managers and digital transformation leaders.

Course Duration: 5 days

Course Modules

Module 1: Foundations of Data Engineering for Generative AI
 

• Introduction to modern data engineering and Generative AI ecosystems.
• Understanding Large Language Models and enterprise AI data requirements.
• Structured, semi-structured, and unstructured data management.
• Data architecture requirements for AI-powered applications.
• Data engineering lifecycle, scalability, and performance optimization.
• Case study: Microsoft's enterprise data infrastructure supporting Azure OpenAI applications.

Module 2: Data Ingestion and Transformation Pipelines
 

• Batch and streaming data ingestion architectures.
• Building ETL and ELT pipelines using Python and SQL.
• Data transformation, normalization, and preprocessing.
• Managing APIs, documents, databases, and enterprise data sources.
• Workflow automation and pipeline orchestration.
• Case study: Netflix's large-scale data ingestion and processing ecosystem.

Module 3: Distributed Data Processing and Big Data Technologies
 

• Apache Spark architecture and distributed computing fundamentals.
• Processing large-scale datasets for Generative AI applications.
• Data partitioning, parallel processing, and optimization.
• Apache Kafka for real-time streaming data pipelines.
• Integrating Spark, Kafka, and cloud storage platforms.
• Case study: Uber's distributed data processing infrastructure for real-time analytics.

Module 4: AI-Ready Data Lakes and Lakehouse Architecture
 

• Designing modern data lake and lakehouse environments.
• Implementing Delta Lake and Apache Iceberg.
• Data storage optimization and scalable architecture design.
• Managing enterprise datasets for LLM applications.
• Cloud data engineering using AWS, Azure, and Google Cloud.
• Case study: Databricks Lakehouse architecture for enterprise AI workloads.

Module 5: Vector Databases and Embedding Engineering
 

• Understanding vector embeddings and semantic representations.
• Generating embeddings from documents and enterprise datasets.
• Implementing Pinecone, Weaviate, and Chroma databases.
• Vector indexing, similarity search, and retrieval optimization.
• Integrating vector databases with Generative AI frameworks.
• Case study: Pinecone-powered semantic search and enterprise knowledge retrieval applications.

Module 6: Retrieval-Augmented Generation Data Pipelines
 

• Understanding Retrieval-Augmented Generation architecture.
• Document ingestion, parsing, chunking, and preprocessing.
• Building embedding and indexing pipelines.
• Implementing semantic retrieval and contextual data enrichment.
• Integrating LangChain and LlamaIndex with enterprise data sources.
• Case study: AI-powered enterprise knowledge retrieval using Microsoft's Azure AI Search.

Module 7: Data Quality, Governance, Security, and Observability
 

• Implementing automated data validation and quality monitoring.
• Enterprise metadata management and data lineage.
• Data privacy, access control, and encryption.
• Managing sensitive information in Generative AI pipelines.
• Pipeline observability, logging, and performance monitoring.
• Case study: Google's enterprise data governance and security practices across cloud data environments.

Module 8: Production Deployment and Optimization of GenAI Data Systems
 

• Deploying production-ready Generative AI data pipelines.
• Implementing data versioning and reproducible workflows.
• Optimizing infrastructure costs, latency, and throughput.
• Integrating data engineering with MLOps and LLMOps.
• Monitoring, scaling, and maintaining AI data infrastructure.
• Case study: Amazon Bedrock and AWS data services for scalable enterprise Generative AI applications.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using modern data engineering tools.
• Hands-on exercises involving real-world data pipelines.
• Group assignments and collaborative architecture design.
• International industry case studies and implementation scenarios.
• Practical projects involving vector databases and Retrieval-Augmented Generation.

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.