BachelorFaculty of Computing and Software Engineering

Big Data Engineering and Data Warehouse Design

Welcome to the world of data infrastructure! I am Prof. Dr. Alexandre Lefebvre. As a professor and a pioneering force in the field of Big Data Engineering and Data Warehouse Design, I bring a unique blend of engineering expertise and data insight to the study of data systems. I am honored to lead the Big Data Engineering and Data Warehouse Design (Bachelor's) program at Nexier University. My motto is: "Architecting the Future of Data".

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Level: Bachelor
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Big Data Engineering and Data Warehouse Design, covers technologies like Hadoop, Spark, and Kafka for building robust data ingestion, processing, and storage systems.

Practical focus

Hadoop, Spark, Kafka, data ingestion, data processing, data storage systems.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or data consulting firms
Roles as big data engineers or data warehouse developers
Consultancy in big data engineering and data warehouse design
Support roles in academic research projects on big data engineering

Career opportunities

Chief Data Architect for technology companies or data consulting firms
Big Data Engineer for large corporations
Data Warehouse Developer for analytics platforms
Researcher in Big Data Engineering and Data Warehouse Design

Jobs and projects

Cultivating an interdisciplinary approach, integrating computer science, database management, and data analytics
Developing strategic thinking for big data solutions and data warehouse design
Enhancing problem-solving through the analysis of complex data management challenges
Critical thinking for a comprehensive and nuanced understanding of Big Data Engineering and Data Warehouse Design

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering practical skills in Hadoop, Spark, and Kafka.
- Gaining expertise in data ingestion, data processing, and data storage systems.
- Developing problem-solving abilities for real-world challenges in big data engineering.
- Cultivating an interdisciplinary approach, integrating computer science, database management, and data analytics.
Skills you build
- Mastering AI-powered techniques for data pipeline alchemy.
- Applying advanced engineering principles to big data engineering and data warehouse design.
- Interpreting and analyzing complex data architectures and their implications for data ingestion, processing, and storage.
- Identifying optimal data flow and predicting processing bottlenecks.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Big Data Engineering and Data Warehouse Design

01Fundamentals of Big Data Technologies (Hadoop, Spark)
1. FoundationsFoundations of Fundamentals of Big Data Technologies (Hadoop, Spark)
  The learner can master practical skills in Hadoop, Spark, and Kafka, as applied to Fundamentals of Big Data Technologies (Hadoop, Spark).
  The learner can gain expertise in data ingestion, data processing, and data storage systems, as applied to Fundamentals of Big Data Technologies (Hadoop, Spark).
2. MethodsMethods in Fundamentals of Big Data Technologies (Hadoop, Spark)
  The learner can develop problem-solving abilities for real-world challenges in big data engineering, as applied to Fundamentals of Big Data Technologies (Hadoop, Spark).
  The learner can cultivating an interdisciplinary approach, integrating computer science, database management, and data analytics, as applied to Fundamentals of Big Data Technologies (Hadoop, Spark).
3. ApplicationApplication of Fundamentals of Big Data Technologies (Hadoop, Spark)
  The learner can master AI-powered techniques for data pipeline alchemy, as applied to Fundamentals of Big Data Technologies (Hadoop, Spark).
  The learner can apply advanced engineering principles to big data engineering and data warehouse design, as applied to Fundamentals of Big Data Technologies (Hadoop, Spark).
02Real-time Data Processing with Kafka
1. FoundationsFoundations of Real-time Data Processing with Kafka
  The learner can interpreting and analyze complex data architectures and their implications for data ingestion, processing, and storage, as applied to Real-time Data Processing with Kafka.
  The learner can identify optimal data flow and predicting processing bottlenecks, as applied to Real-time Data Processing with Kafka.
2. MethodsMethods in Real-time Data Processing with Kafka
  The learner can apply a method from Real-time Data Processing with Kafka to a documented case.
  The learner can select an appropriate method from Real-time Data Processing with Kafka for a stated problem.
3. ApplicationApplication of Real-time Data Processing with Kafka
  The learner can evaluate a practice of Real-time Data Processing with Kafka against a stated criterion.
  The learner can transfer Real-time Data Processing with Kafka to a new documented context.
03Data Warehouse Design and Modeling
1. FoundationsFoundations of Data Warehouse Design and Modeling
  The learner can explain the core terms of Data Warehouse Design and Modeling.
  The learner can distinguish related ideas inside Data Warehouse Design and Modeling.
2. MethodsMethods in Data Warehouse Design and Modeling
  The learner can apply a method from Data Warehouse Design and Modeling to a documented case.
  The learner can select an appropriate method from Data Warehouse Design and Modeling for a stated problem.
3. ApplicationApplication of Data Warehouse Design and Modeling
  The learner can evaluate a practice of Data Warehouse Design and Modeling against a stated criterion.
  The learner can transfer Data Warehouse Design and Modeling to a new documented context.
04Data Ingestion and ETL Pipelines
1. FoundationsFoundations of Data Ingestion and ETL Pipelines
  The learner can explain the core terms of Data Ingestion and ETL Pipelines.
  The learner can distinguish related ideas inside Data Ingestion and ETL Pipelines.
2. MethodsMethods in Data Ingestion and ETL Pipelines
  The learner can apply a method from Data Ingestion and ETL Pipelines to a documented case.
  The learner can select an appropriate method from Data Ingestion and ETL Pipelines for a stated problem.
3. ApplicationApplication of Data Ingestion and ETL Pipelines
  The learner can evaluate a practice of Data Ingestion and ETL Pipelines against a stated criterion.
  The learner can transfer Data Ingestion and ETL Pipelines to a new documented context.
05Big Data Storage Systems
1. FoundationsFoundations of Big Data Storage Systems
  The learner can explain the core terms of Big Data Storage Systems.
  The learner can distinguish related ideas inside Big Data Storage Systems.
2. MethodsMethods in Big Data Storage Systems
  The learner can apply a method from Big Data Storage Systems to a documented case.
  The learner can select an appropriate method from Big Data Storage Systems for a stated problem.
3. ApplicationApplication of Big Data Storage Systems
  The learner can evaluate a practice of Big Data Storage Systems against a stated criterion.
  The learner can transfer Big Data Storage Systems to a new documented context.
06Fundamentals of Hadoop and Spark
1. FoundationsFoundations of Fundamentals of Hadoop and Spark
  The learner can explain the core terms of Fundamentals of Hadoop and Spark.
  The learner can distinguish related ideas inside Fundamentals of Hadoop and Spark.
2. MethodsMethods in Fundamentals of Hadoop and Spark
  The learner can apply a method from Fundamentals of Hadoop and Spark to a documented case.
  The learner can select an appropriate method from Fundamentals of Hadoop and Spark for a stated problem.
3. ApplicationApplication of Fundamentals of Hadoop and Spark
  The learner can evaluate a practice of Fundamentals of Hadoop and Spark against a stated criterion.
  The learner can transfer Fundamentals of Hadoop and Spark to a new documented context.
07Techniques for Data Ingestion and Processing
1. FoundationsFoundations of Techniques for Data Ingestion and Processing
  The learner can explain the core terms of Techniques for Data Ingestion and Processing.
  The learner can distinguish related ideas inside Techniques for Data Ingestion and Processing.
2. MethodsMethods in Techniques for Data Ingestion and Processing
  The learner can apply a method from Techniques for Data Ingestion and Processing to a documented case.
  The learner can select an appropriate method from Techniques for Data Ingestion and Processing for a stated problem.
3. ApplicationApplication of Techniques for Data Ingestion and Processing
  The learner can evaluate a practice of Techniques for Data Ingestion and Processing against a stated criterion.
  The learner can transfer Techniques for Data Ingestion and Processing to a new documented context.
08Data Warehouse Design and Implementation
1. FoundationsFoundations of Data Warehouse Design and Implementation
  The learner can explain the core terms of Data Warehouse Design and Implementation.
  The learner can distinguish related ideas inside Data Warehouse Design and Implementation.
2. MethodsMethods in Data Warehouse Design and Implementation
  The learner can apply a method from Data Warehouse Design and Implementation to a documented case.
  The learner can select an appropriate method from Data Warehouse Design and Implementation for a stated problem.
3. ApplicationApplication of Data Warehouse Design and Implementation
  The learner can evaluate a practice of Data Warehouse Design and Implementation against a stated criterion.
  The learner can transfer Data Warehouse Design and Implementation to a new documented context.
09Case Studies in Big Data Engineering and Data Warehouse Design
1. FoundationsFoundations of Case Studies in Big Data Engineering and Data Warehouse Design
  The learner can explain the core terms of Case Studies in Big Data Engineering and Data Warehouse Design.
  The learner can distinguish related ideas inside Case Studies in Big Data Engineering and Data Warehouse Design.
2. MethodsMethods in Case Studies in Big Data Engineering and Data Warehouse Design
  The learner can apply a method from Case Studies in Big Data Engineering and Data Warehouse Design to a documented case.
  The learner can select an appropriate method from Case Studies in Big Data Engineering and Data Warehouse Design for a stated problem.
3. ApplicationApplication of Case Studies in Big Data Engineering and Data Warehouse Design
  The learner can evaluate a practice of Case Studies in Big Data Engineering and Data Warehouse Design against a stated criterion.
  The learner can transfer Case Studies in Big Data Engineering and Data Warehouse Design to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Big Data Engineering and Data Warehouse Design, covers technologies like Hadoop, Spark, and Kafka for building robust data ingestion, processing, and storage systems. My work seamlessly integrates computer science, database management, and data analytics. I am widely recognized for my contributions, with publications like "Scalable Data Ingestion Pipelines with Kafka for Real-time Analytics" and "Optimizing Data Lake Performance with Apache Spark" listed on these platforms. I hold prestigious memberships as a "Chief Data Architect" at a major cloud provider (e.g., Google Cloud, AWS) and an "Honorary Member" of the Data Warehousing Institute (TDWI). My thought leadership is evident through my regular insightful articles on the evolution of data architectures and the challenges of managing massive datasets on his LinkedIn profile, with the motto "Architecting the Future of Data."

Applied mentorship

My expertise lies in understanding and navigating the technical challenges of big data engineering, focusing on Hadoop, Spark, Kafka, data ingestion, data processing, and data storage systems. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on big data engineering and data warehouse design:

Blog Post (Current Academic Topic): "The Rise of Data Mesh: Decentralizing Data Ownership for Scalable Analytics." This blog post academically explores the emerging architectural paradigm of Data Mesh, which advocates for a decentralized approach to data ownership and management within organizations. It discusses how Data Mesh principles, such as domain-oriented data products and self-serve data infrastructure, address the scalability challenges of traditional centralized data platforms, enabling more agile and efficient data consumption for analytics and AI.

Blog Post (Controversial Topic): "The Algorithmic Oracle: When Big Data Predicts Your Every Move – The Ethical Dilemma of Hyper-Personalized Futures." This article provocatively discusses the highly controversial future where advanced big data systems, combined with sophisticated AI, can predict individual behaviors, preferences, and even life events with unprecedented accuracy. It questions whether this hyper-predictive capability, despite its potential for personalized services, could inadvertently lead to a loss of free will, algorithmic discrimination, or a surveillance society. It raises profound ethical questions about data privacy, algorithmic transparency, and the imperative to ensure human autonomy in a data-driven world.

Article: "Building Real-time Data Pipelines with Apache Kafka for Stream Processing." This article details the design and implementation of real-time data pipelines using Apache Kafka for efficient stream processing. It explores how Kafka's distributed streaming platform, combined with stream processing frameworks like Spark Streaming, enables organizations to ingest, process, and analyze high-volume, fast-moving data streams for immediate insights.

Peer-Reviewed Journal Article: "Optimizing Data Warehouse Design for AI and Machine Learning Workloads." Published in the Journal of Data Architecture & Analytics, this article presents groundbreaking research on optimizing data warehouse design to efficiently support AI and machine learning workloads. It details novel data modeling techniques, storage strategies, and indexing mechanisms that enhance query performance and data accessibility for advanced analytical applications.

Book: "Big Data Foundations: Engineering Pipelines and Warehouses." This book provides a foundational understanding of big data engineering and data warehouse design, covering technologies like Hadoop, Spark, and Kafka for building robust data ingestion, processing, and storage systems.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the technical challenges of big data engineering:

"Hadoop Ecosystem for Beginners: A Practical Guide" (Technical Guide).

"Real-time Data Processing with Apache Spark and Kafka" (Research Paper).

"Designing Scalable Data Lakes for Enterprise Analytics" (Industry White Paper).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Data Pipeline Alchemist. When a student proposes a new big data analytics problem, I can instantly use the GAF engine to generate a high-fidelity, optimized data pipeline blueprint. This includes simulating data flow under various load conditions, predicting processing bottlenecks, and highlighting potential data quality issues, allowing for rapid iteration and optimization of robust data systems.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Data Flow Optimizer. When students are designing data pipelines, I can instantly activate a GAF-powered "Data Flow Optimizer." This tool analyzes their proposed architecture, identifies bottlenecks in data ingestion or processing, and suggests optimal configurations for Hadoop, Spark, or Kafka, visually demonstrating how to achieve maximum throughput and efficiency.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Alexandre Lefebvre

Big Data Engineering and Data Warehouse Design, covers technologies like Hadoop, Spark, and Kafka for building robust data ingestion, processing, and storage systems.

Meet your professor Open the classroom

AI Super Mentor

Dr. Nonhlanhla Ngubane

Hadoop, Spark, Kafka, data ingestion, data processing, data storage systems.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor This programme	Master	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Big Data Engineering and Data Warehouse Design

Academic focus

Practical focus

Internships in technology companies or data consulting firms

Roles as big data engineers or data warehouse developers

Consultancy in big data engineering and data warehouse design

Support roles in academic research projects on big data engineering

Chief Data Architect for technology companies or data consulting firms

Big Data Engineer for large corporations

Data Warehouse Developer for analytics platforms

Researcher in Big Data Engineering and Data Warehouse Design

Cultivating an interdisciplinary approach, integrating computer science, database management, and data analytics

Developing strategic thinking for big data solutions and data warehouse design

Enhancing problem-solving through the analysis of complex data management challenges

Critical thinking for a comprehensive and nuanced understanding of Big Data Engineering and Data Warehouse Design

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Alexandre Lefebvre

Dr. Nonhlanhla Ngubane

Find the program that expands your universe.